2018 IEEE 12th International Symposium on Applied Computational Intelligence and Informatics (SACI)最新文献

CLEMI-Imputation Evaluation CLEMI-Imputation评价

2018 IEEE 12th International Symposium on Applied Computational Intelligence and Informatics (SACI) Pub Date : 2018-08-20 DOI: 10.1109/SACI.2018.8440981

Anthony Chapman, Wei Pang, G. Coghill

引用次数: 1

Fine Tuning Deep Boltzmann Machines Through Meta-Heuristic Approaches 通过元启发式方法微调深度玻尔兹曼机

2018 IEEE 12th International Symposium on Applied Computational Intelligence and Informatics (SACI) Pub Date : 2018-08-20 DOI: 10.1109/SACI.2018.8440959

L. A. Passos, D. Rodrigues, J. Papa

引用次数: 14

Content Driven Engineering Model System for Cyber Physical Systems 内容驱动的网络物理系统工程模型系统

2018 IEEE 12th International Symposium on Applied Computational Intelligence and Informatics (SACI) Pub Date : 2018-08-20 DOI: 10.1109/SACI.2018.8440958

L. Horváth, Andrea Serester

{"title":"Content Driven Engineering Model System for Cyber Physical Systems","authors":"L. Horváth, Andrea Serester","doi":"10.1109/SACI.2018.8440958","DOIUrl":"https://doi.org/10.1109/SACI.2018.8440958","url":null,"abstract":"Engineering model system is complex description and representation of an engineering structure. It serves as active media for all engineering activities during lifecycle of engineering structure. By now, engineering model system is capable of representation systems operated engineering structure and it can be configured to include organized experiments for engineering related research and development activities. Active knowledge is represented in engineering model system with the demand for integration of theory and practice. Recently, systems operated engineering structure is modeled as cyber physical system. Consequently, engineering modeling capabilities must be appropriate for description and representation of cooperating systems considering cyber and physical units. Despite their suitability for modeling systems, former analysis by the authors revealed that current engineering model systems would require organized knowledge background to support repeated and changed decisions on engineering object parameters in description and representation of engineering structure. To establish this background, former research introduced and defined a new concept called as information content. Model structure of information content was established as new integrated unit of engineering model system. Purpose of this paper is to introduce recent research results in rethinking and restructuring of information content for engineering model system which describe and represent cyber physical system. For that reason, extended engineering model system is given to answer how cyber physical system changes demand for information content in engineering model system. This is followed by introduction and discussion of cyber physical system eligible extended structure of information content representations. Connections are defined between information content and cyber physical system. Finally, implementation issues are discussed.","PeriodicalId":126087,"journal":{"name":"2018 IEEE 12th International Symposium on Applied Computational Intelligence and Informatics (SACI)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129229973","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: 2

Seam Carving Detection Using Convolutional Neural Networks 基于卷积神经网络的接缝雕刻检测

2018 IEEE 12th International Symposium on Applied Computational Intelligence and Informatics (SACI) Pub Date : 2018-08-20 DOI: 10.1109/SACI.2018.8441016

Luiz Fernandoda Silva Cieslak, K. Costa, J. Papa

引用次数: 9

Parkinson Disease Identification Using Residual Networks and Optimum-Path Forest 基于残差网络和最优路径森林的帕金森病识别

2018 IEEE 12th International Symposium on Applied Computational Intelligence and Informatics (SACI) Pub Date : 2018-08-20 DOI: 10.1109/SACI.2018.8441012

L. A. Passos, C. R. Pereira, Edmar R. S. Rezende, Tiago J. Carvalho, S. Weber, C. Hook, J. Papa

{"title":"Parkinson Disease Identification Using Residual Networks and Optimum-Path Forest","authors":"L. A. Passos, C. R. Pereira, Edmar R. S. Rezende, Tiago J. Carvalho, S. Weber, C. Hook, J. Papa","doi":"10.1109/SACI.2018.8441012","DOIUrl":"https://doi.org/10.1109/SACI.2018.8441012","url":null,"abstract":"Known as one of the most significant neurodegenerative diseases of the central nervous system, Parkinson's disease (PD) has a combination of several symptoms, such as tremor, postural instability, loss of movements, depression, anxiety, and dementia, among others. For the medicine, to point an exam that can diagnose a patient with such illness is challenging due to the symptoms that are easily related to other diseases. Therefore, developing computational methods capable of identifying PD in its early stages has been of paramount importance in the scientific community. Thence, this paper proposes to use a deep neural network called ResNet-50 to learn the patterns and extract features from images draw by patients. Afterwards, the Optimum-Path Forest (OPF) classifier is employed to identify Parkinson's disease automatically, being the results compared against two well-known classifiers, i.e., Support Vector Machines and the Bayes, as well as the ones provided by ResNet-50 itself. The experiments showed promising results concerning OPF, reachinz over 96% of identification rate.","PeriodicalId":126087,"journal":{"name":"2018 IEEE 12th International Symposium on Applied Computational Intelligence and Informatics (SACI)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124478076","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: 30

Prediction of the Survival of Patients with Cardiac Failure by Using Soft Computing Techniques 应用软计算技术预测心力衰竭患者的生存期

2018 IEEE 12th International Symposium on Applied Computational Intelligence and Informatics (SACI) Pub Date : 2018-08-20 DOI: 10.1109/SACI.2018.8440931

Kinan Morani, G. Eigner, T. Ferenci, L. Kovács, Ş. Engin

引用次数: 0

Granular Computing and Sequential Analysis of Deep Embeddings in Fast Still-to-Video Face Recognition 快速静止到视频人脸识别中深度嵌入的颗粒计算和序列分析

2018 IEEE 12th International Symposium on Applied Computational Intelligence and Informatics (SACI) Pub Date : 2018-05-17 DOI: 10.1109/SACI.2018.8441009

A. Savchenko

{"title":"Granular Computing and Sequential Analysis of Deep Embeddings in Fast Still-to-Video Face Recognition","authors":"A. Savchenko","doi":"10.1109/SACI.2018.8441009","DOIUrl":"https://doi.org/10.1109/SACI.2018.8441009","url":null,"abstract":"This paper is focused on still-to-video face recognition with large number of subjects based on computation of distances between high-dimensional embeddings extracted using deep convolution neural networks. We propose to utilize granular structures and sequentially process granular representations of all frames of the input video. The coarse-grained granules include only low number of the first principal components of deep embeddings. The representation of each frame at finer granularity levels is matched with the representations of photos of only those individuals, for whom the decision at previous levels was reliable. The reliability is checked by thresholding the ratio of distance between reference instance and input frame to the minimal distance. As a result, the photos of all unreliable individuals are not examined anymore for a particular frame at the next levels with finer granularity. Decisions for all frames are united into a candidate set of identities, and the maximal a-posterior final decision is chosen. The experimental study with the LFW, YTF and IJB-A datasets and the state-of-the-art deep embeddings demonstrated that the proposed approach is 2–10 times faster than conventional methods.","PeriodicalId":126087,"journal":{"name":"2018 IEEE 12th International Symposium on Applied Computational Intelligence and Informatics (SACI)","volume":"97 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123072394","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: 3

Privacy of Clients' Locations in Big Data and Cloud Computing 大数据和云计算中客户位置的隐私

2018 IEEE 12th International Symposium on Applied Computational Intelligence and Informatics (SACI) Pub Date : 2018-05-17 DOI: 10.1109/SACI.2018.8440940

Imad Ali Hassoon, N. Tapus, Anwar Chitheer Jasim

{"title":"Privacy of Clients' Locations in Big Data and Cloud Computing","authors":"Imad Ali Hassoon, N. Tapus, Anwar Chitheer Jasim","doi":"10.1109/SACI.2018.8440940","DOIUrl":"https://doi.org/10.1109/SACI.2018.8440940","url":null,"abstract":"Amid the very hot issues, nowadays, the one related to Locations' privacy (GPS related) finds itself in top position. When it comes to talking about clients' locations in cloud or big data, the probable risk to privacy of clients' location is one of the major challenges should to be faced. In the recent years, a lot of developers and researchers have been paying attention to improve methods to provide privacy for data of clients' locations which it always processed by third-party. Big data could be like a puzzle for many researchers if they didn't understand it in the correct-side. Big data has to be understood as the process of gathering as much data as can be permitted in order to collect knowledge out of them (ideally in ground-breaking ways). so, this concept gives us attention that is privacy of clients' locations in cloud or big data could be under risks if it is used to collect knowledge or sell it to third-party. In our research, we try to show how we have implemented our algorithm (Diff-Anonym) in real data set (available at http://openaddresses.io) as to offer privacy for the clients' Locations in Big Data and cloud computing, as well as to improve our previous work which was simulation in normal data that appeared little differences in the results.","PeriodicalId":126087,"journal":{"name":"2018 IEEE 12th International Symposium on Applied Computational Intelligence and Informatics (SACI)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123332754","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: 4

Programming Concepts in the Silver Code Guide for Elders 长者银色代码指南中的编程概念

2018 IEEE 12th International Symposium on Applied Computational Intelligence and Informatics (SACI) Pub Date : 2018-05-17 DOI: 10.1109/SACI.2018.8441001

O. Lupse, Ciprian-Bogdan Chirila, H. Ciocarlie

引用次数: 1

A Comparative Analysis of Monitoring Concerns Implementation in Object Oriented Systems 面向对象系统中监控关注点实现的比较分析

2018 IEEE 12th International Symposium on Applied Computational Intelligence and Informatics (SACI) Pub Date : 2018-05-17 DOI: 10.1109/SACI.2018.8440997

G. Cojocar, A. Guran

引用次数: 3