2017 Intelligent Systems Conference (IntelliSys)最新文献_第4页

Implementation of evolutionary algorithms in vehicular ad-hoc network for cluster optimization 进化算法在车载自组织网络中的实现

2017 Intelligent Systems Conference (IntelliSys) Pub Date : 2017-09-01 DOI: 10.1109/INTELLISYS.2017.8324281

M. Fahad, Farhan Aadil, Sadia Ejaz, Asad Ali

{"title":"Implementation of evolutionary algorithms in vehicular ad-hoc network for cluster optimization","authors":"M. Fahad, Farhan Aadil, Sadia Ejaz, Asad Ali","doi":"10.1109/INTELLISYS.2017.8324281","DOIUrl":"https://doi.org/10.1109/INTELLISYS.2017.8324281","url":null,"abstract":"Among many other sub-types, one sub-type of ad hoc network is Vehicular ad hoc Network (VANET). VANET can be further categorized in sub-domains like Vehicle to Infrastructure (V2I), Vehicle to Vehicle (V2V), Vehicle to pedestrian or other equipment (V2X) and Hybrid (V2V+V2I+V2X). V2V communication is the primary focus of this paper. Different methodologies are available in the literature for optimization of V2V communication. Clustering is one of them, in clustering vehicles, the same vicinity are grouped together for efficient communication. Different evolutionary algorithms for clustering already have been implemented to route information among nodes. Two evolutionary algorithms are applied for optimizing communication among the vehicles and the clustering problem in the VANETs. The bio inspired evolutionary algorithms are Comprehensive Learning Particle Swarm Optimization (CLPSO) and Multi-Objective Particle Swarm Optimization (MOPSO). After implementation, comparison for the mentioned algorithms is used to depict the results. The experimental results show that CLPSO is providing better results than MOPSO.","PeriodicalId":131825,"journal":{"name":"2017 Intelligent Systems Conference (IntelliSys)","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":"130901417","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: 14

A modified penalty function in fuzzy clustering algorithm 一种改进的模糊聚类罚函数算法

2017 Intelligent Systems Conference (IntelliSys) Pub Date : 2017-09-01 DOI: 10.1109/INTELLISYS.2017.8324332

Md. Kibria Saroare, Md. Syadus Sefat, S. Sen, M. Shahjahan

引用次数: 1

Design and development of a skinny bidirectional soft glove for post-stroke hand rehabilitation 一种用于中风后手部康复的薄型双向软手套的设计与开发

2017 Intelligent Systems Conference (IntelliSys) Pub Date : 2017-09-01 DOI: 10.1109/INTELLISYS.2017.8324248

Boran Wang, A. McDaid, K. Aw, M. Biglari-Abhari

引用次数: 6

Real-world large-step stair-climbing for small humanoids using evolutionary computation 基于进化计算的小型人形机器人在现实世界中的大台阶爬楼梯

2017 Intelligent Systems Conference (IntelliSys) Pub Date : 2017-09-01 DOI: 10.1109/INTELLISYS.2017.8324253

V. Berezhnoy, A. Klimchik, N. Mavridis

引用次数: 1

An hybrid front-end for robust speaker identification under noisy conditions 一种用于噪声条件下稳健说话人识别的混合前端

2017 Intelligent Systems Conference (IntelliSys) Pub Date : 2017-09-01 DOI: 10.1109/INTELLISYS.2017.8324215

El Bachir Tazi, Noureddine El Makhfi

引用次数: 6

A method of detecting a malicious actions using HTTP and FTP protocols 一种使用HTTP和FTP协议检测恶意操作的方法

2017 Intelligent Systems Conference (IntelliSys) Pub Date : 2017-09-01 DOI: 10.1109/INTELLISYS.2017.8324264

Dmitry S. Silnov, A. O. Prokofiev, G. Berezovskaya, V. A. Perevozchikov, S. S. Troitskiy, I. U. Shumakov

引用次数: 1

How efficient is Twitter: Predicting 2012 U.S. presidential elections using Support Vector Machine via Twitter and comparing against Iowa Electronic Markets Twitter的效率如何:通过Twitter使用支持向量机预测2012年美国总统大选，并与爱荷华州电子市场进行比较

2017 Intelligent Systems Conference (IntelliSys) Pub Date : 2017-09-01 DOI: 10.1109/INTELLISYS.2017.8324363

A. Attarwala, Stanko Dimitrov, Amer Obeidi

{"title":"How efficient is Twitter: Predicting 2012 U.S. presidential elections using Support Vector Machine via Twitter and comparing against Iowa Electronic Markets","authors":"A. Attarwala, Stanko Dimitrov, Amer Obeidi","doi":"10.1109/INTELLISYS.2017.8324363","DOIUrl":"https://doi.org/10.1109/INTELLISYS.2017.8324363","url":null,"abstract":"We test the efficient market hypothesis to see if Twitter aggregates information faster than a real-money prediction market. We use Support Vector Machines (SVMs), a supervised learning algorithm, to predict the outcome of the 2012 U.S. presidential elections via Twitter data. We then compare the prediction from SVM against the Iowa Electronic Markets (IEM). A total of 40 million unique tweets were collected and analyzed between September 29th 2012 and November 6th 2012. We observe: 1) The IEM is efficient on all the above days as per the semi-strong efficient market hypothesis definition [1]. SVM does not out predict the IEM. 2) The SVM prediction results are positively correlated with the IEM and predicts Obama winning the election, implying that Twitter can be considered as a valid source in predicting US presidential election outcomes. Using the Granger causality test, no causal relationship was inferred between the two-time series. 3) The candidate frequency count distribution independent of any sentiment analysis on all days is also positively correlated with IEM and SVM. Using Granger causality test, we determined that IEM statistically causes the candidate frequency count distribution in Twitter at the 1% level.","PeriodicalId":131825,"journal":{"name":"2017 Intelligent Systems Conference (IntelliSys)","volume":"95 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":"126246954","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: 15

Presence analytics: Detecting classroom-based social patterns using WLAN traces 状态分析:使用WLAN跟踪检测基于教室的社会模式

2017 Intelligent Systems Conference (IntelliSys) Pub Date : 2017-09-01 DOI: 10.1109/INTELLISYS.2017.8324316

M. H. S. Eldaw, M. Levene, George Roussos

{"title":"Presence analytics: Detecting classroom-based social patterns using WLAN traces","authors":"M. H. S. Eldaw, M. Levene, George Roussos","doi":"10.1109/INTELLISYS.2017.8324316","DOIUrl":"https://doi.org/10.1109/INTELLISYS.2017.8324316","url":null,"abstract":"We demonstrate how density-based clustering of WLAN traces can be utilised to discover social groups of students within a university campus. For this purpose we deploy a temporally restricted version of the Social-DBSCAN algorithm [2] to discover social groups of students who attend the same classes. We detect the existence of social relationships between the students attending the same class by analysing their behaviour of visit during break-times. The intuition is that if a group of two or more students are friends, who attend the same classes, then they are likely to be socialising/meeting more often at locations such as the Coffee-shop during break-times. By leveraging information extracted from the timetable as well as the teaching practices at the case-study university, we inform our model about the duration of the break-times. Utilising a large data set of Eduroam traces, collected at the main site of the case-study institution, we chose as a proof concept, a set of locations for the evaluation of the proposed method, which we successfully employed to detect the social groups of students who attended regular classes at those chosen locations.","PeriodicalId":131825,"journal":{"name":"2017 Intelligent Systems Conference (IntelliSys)","volume":"6 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":"133737820","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

The cyber teamwork system based on cloud to maximize the teamwork capability 基于云的网络团队协作系统，实现团队协作能力最大化

2017 Intelligent Systems Conference (IntelliSys) Pub Date : 2017-09-01 DOI: 10.1109/INTELLISYS.2017.8324317

Narungsun Wilaisakoolyong

{"title":"The cyber teamwork system based on cloud to maximize the teamwork capability","authors":"Narungsun Wilaisakoolyong","doi":"10.1109/INTELLISYS.2017.8324317","DOIUrl":"https://doi.org/10.1109/INTELLISYS.2017.8324317","url":null,"abstract":"The cyber teamwork system based on cloud was created to support working group and maximize the teamwork capability. The cyber teamwork system comprised of five components. They were Collaboration, Communication, Project management, Document management and Operation control. The main aims of this research were (1) to develop the cyber teamwork system based on cloud (2) to maximize the teamwork capability (3) to evaluate the quality of each team's project and (4) to evaluate satisfaction of system users. Researcher brought this system to trial with 20 teamwork groups. Each team used the cyber teamwork system based on cloud to do their own project. The experimental design was Quasi Experimental Pre-test Post-test Control Group Design. The statistics employed for analyzing the data were mean, standard deviation and independent t-test. The research results show that there were 13 of 20 teamwork's projects which evaluated over a “very good” level. The teamwork ability after using the cyber teamwork system was higher than that before using the system at 0.05 level of significance. The satisfactions of system users were evaluated at a good level.","PeriodicalId":131825,"journal":{"name":"2017 Intelligent Systems Conference (IntelliSys)","volume":"47 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":"116597349","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: 1

Smart city project using proximity marketing technology 使用邻近营销技术的智慧城市项目

2017 Intelligent Systems Conference (IntelliSys) Pub Date : 2017-09-01 DOI: 10.1109/INTELLISYS.2017.8324288

Lamya Albraheem, Amal Al-Abdulkarim, Arwa Al-Dosari, Leena Al-Abdulkarim, Raghad Al-Khudair, Wafa Al-Jasser, Weam Al-Angari

{"title":"Smart city project using proximity marketing technology","authors":"Lamya Albraheem, Amal Al-Abdulkarim, Arwa Al-Dosari, Leena Al-Abdulkarim, Raghad Al-Khudair, Wafa Al-Jasser, Weam Al-Angari","doi":"10.1109/INTELLISYS.2017.8324288","DOIUrl":"https://doi.org/10.1109/INTELLISYS.2017.8324288","url":null,"abstract":"Proximity Marketing is a technology that has been used for developing smart cities and recently is given a huge attention. Proximity Marketing is defined as delivering advertisements to users according to their locations. In fact, customers often find it difficult to take advantage of offers because advertisements can be invisible like street signs or can be irrelevant like SMS messages. Direct advertising can be deliverd using many technologies, such as GPS, RFID, NFC, and Wi-Fi. However, they have different limitations and implementation issues. In this paper, we plan to develop a system that uses iBeacon technology which can be used for delivering advertisements to passengers of public transportation or any individual who happened to be near the public transportation stations. Therefore, a review of the existing projects that were developed in different countries in the field of smart public transportation and proximity marketing are discussed, and a comparison between applications that use iBeacon technology in the same field is provided. Morevoer, the best features for designing this project are considered.","PeriodicalId":131825,"journal":{"name":"2017 Intelligent Systems Conference (IntelliSys)","volume":"208 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":"131512655","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