2018 IEEE Symposium Series on Computational Intelligence (SSCI)最新文献_第10页

Cascading classifier application for topology prediction of TMB proteins 级联分类器在TMB蛋白拓扑预测中的应用

2018 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2018-11-01 DOI: 10.1109/SSCI.2018.8628845

H. Kazemian, Cedric Maxime Grimaldi

{"title":"Cascading classifier application for topology prediction of TMB proteins","authors":"H. Kazemian, Cedric Maxime Grimaldi","doi":"10.1109/SSCI.2018.8628845","DOIUrl":"https://doi.org/10.1109/SSCI.2018.8628845","url":null,"abstract":"This paper is concerned with the use of a cascading classifier for trans-membrane beta-barrel topology prediction analysis. Most of novel drug design requires the use of membrane proteins. Trans-membrane proteins have key roles such as active transport across the membrane and signal transduction among other functions. Given their key roles, understanding their structures mechanisms and regulation at the level of molecules with the use of computational modeling is essential. In the field of bioinformatics, many years have been spent on the trans-membrane protein structure prediction focusing on the alpha-helix membrane proteins. Technological developments have been increasingly utilized in order to understand in more details membrane protein function and structure. Various methodologies have been developed for the prediction of TMB (trans-membrane beta-barrel) proteins topology however the use of cascading classifier has not been fully explored. This research presents a novel approach for TMB topology prediction. The MATLAB computer simulation results show that the proposed methodology predicts trans-membrane topologies with high accuracy for randomly selected proteins.","PeriodicalId":235735,"journal":{"name":"2018 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"319 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133959974","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

A Time Efficient Model for Region of Interest Extraction in Real Time Traffic Signs Recognition System 实时交通标志识别系统中一种高效的兴趣区域提取模型

2018 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2018-11-01 DOI: 10.1109/SSCI.2018.8628874

Fareed Qararyah, Yousef-Awwad Daraghmi, E. Daraghmi, S. Rajora, Chin-Teng Lin, M. Prasad

{"title":"A Time Efficient Model for Region of Interest Extraction in Real Time Traffic Signs Recognition System","authors":"Fareed Qararyah, Yousef-Awwad Daraghmi, E. Daraghmi, S. Rajora, Chin-Teng Lin, M. Prasad","doi":"10.1109/SSCI.2018.8628874","DOIUrl":"https://doi.org/10.1109/SSCI.2018.8628874","url":null,"abstract":"Computation intelligence plays a major role in developing intelligent vehicles, which contains a Traffic Sign Recognition (TSR) system for increasing vehicle safety. Traffic sign recognition systems consist of an initial phase called Traffic Sign Detection (TSD), where images and colors are segmented and fed to the recognition phase. The most challenging process in TSR systems in terms of time consumption is the detection phase. The previous studies proposed different models for traffic sign detection, however, the computation time of these models still requires improvement for enabling real time systems. Therefore, this paper focuses on the computational time and proposes a novel time efficient color segmentation model based on logistic regression. This paper uses RGB color space as the domain to extract the features of our hypothesis; this has boosted the speed of the proposed model, since no color conversion is needed. The trained segmentation classifier is tested on 1000 traffic sign images taken in different lighting conditions. The experimental results show that the proposed model segmented 974 of these images correctly and in a time less than one-fifth of the time needed by any other robust segmentation methods.","PeriodicalId":235735,"journal":{"name":"2018 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123940730","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

Structured Prediction Networks through Latent Cost Learning 基于潜在成本学习的结构化预测网络

2018 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2018-11-01 DOI: 10.1109/SSCI.2018.8628625

R. Milidiú, R. Rocha

引用次数: 1

Combinations in predictive analytics by using machine learning 通过使用机器学习在预测分析中的组合

2018 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2018-11-01 DOI: 10.1109/SSCI.2018.8628755

Emrah Gulay, O. Duru

引用次数: 1

A Two-Factor Authentication Scheme based on Negative Databases 一种基于负数据库的双因素认证方案

2018 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2018-11-01 DOI: 10.1109/SSCI.2018.8628732

Ran Liu, Xiang Wang, Can Wang

引用次数: 0

Discussing Different Clustering Methods for the Aggregation of Demand Response and Distributed Generation 需求响应与分布式发电聚合的不同聚类方法探讨

2018 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2018-11-01 DOI: 10.1109/SSCI.2018.8628781

C. Silva, P. Faria, Z. Vale

引用次数: 11

Prediction based Policy setting by finding significance of Attributes from the Ontological Framework in Agricultural domain 基于农业领域本体框架中属性意义的预测策略设置

2018 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2018-11-01 DOI: 10.1109/SSCI.2018.8628708

V. S. Pruthvi, Rohit Naik, B. Kumar

引用次数: 0

Cooperative Coevolutionary Approximation in HOG-based Human Detection Embedded System 基于hog的嵌入式人体检测系统协同进化逼近

2018 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2018-11-01 DOI: 10.1109/SSCI.2018.8628910

Michal Wiglasz, L. Sekanina

引用次数: 5

A Differential Evolution Approach for the Reliability Constrained Unit Commitment Problem 可靠性约束机组承诺问题的差分演化方法

2018 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2018-11-01 DOI: 10.1109/SSCI.2018.8628693

Vasilios Tsalavoutis, Constantinos Vrionis, A. Tolis, Dimitrios Plataniotis

{"title":"A Differential Evolution Approach for the Reliability Constrained Unit Commitment Problem","authors":"Vasilios Tsalavoutis, Constantinos Vrionis, A. Tolis, Dimitrios Plataniotis","doi":"10.1109/SSCI.2018.8628693","DOIUrl":"https://doi.org/10.1109/SSCI.2018.8628693","url":null,"abstract":"In this paper, an efficient approach is proposed to optimize the Unit Commitment Problem (UCP) considering the unreliability of the generating units and the load forecast uncertainty. Reliability indices such as the Loss of Load Probability (LOLP) and the Expected Energy Not Served (EENS) are included in the formulation of the UCP to implicitly assess the required spinning reserve of the system. The method is based on the Differential Evolution (DE) algorithm combined with a hereby proposed series of problem specific repair mechanisms, which enhance the algorithm's performance. The approach is tested on the IEEE Reliability Test System (IEEE RTS), which comprises 26 thermal units. The impact of the units' unreliability and of the load forecast uncertainty on the required reserve and on the total operation cost is evaluated. A benchmarking against previously proposed algorithms reveals that the proposed method provides consistently solutions of lower cost in competitive time. Moreover, the algorithm is applied on systems of larger size, demonstrating an efficient and robust performance.","PeriodicalId":235735,"journal":{"name":"2018 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"270 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114477122","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

A Linguistically Interpretable ELANFIS for Classification Problems 用于分类问题的语言可解释ELANFIS

2018 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2018-11-01 DOI: 10.1109/SSCI.2018.8628689

C. Pramod, G. Pillai

引用次数: 1