{"title":"Upper and lower general aggregation operators based on a strong fuzzy metric","authors":"P. Orlovs, S. Asmuss","doi":"10.1142/9789813273238_0022","DOIUrl":"https://doi.org/10.1142/9789813273238_0022","url":null,"abstract":"","PeriodicalId":259425,"journal":{"name":"Data Science and Knowledge Engineering for Sensing Decision Support","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122773043","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}
{"title":"Social influenced decision making: A belief-based model analysis","authors":"Lei Ni, Yu-wang Chen, O. Bruijn","doi":"10.1142/9789813273238_0053","DOIUrl":"https://doi.org/10.1142/9789813273238_0053","url":null,"abstract":"","PeriodicalId":259425,"journal":{"name":"Data Science and Knowledge Engineering for Sensing Decision Support","volume":"216 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126987225","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}
{"title":"Energy optimisation in cloud servers using a static threshold VM consolidation technique (STVMC)","authors":"B. Ahmad, S. McClean, D. Charles, G. Parr","doi":"10.1142/9789813273238_0018","DOIUrl":"https://doi.org/10.1142/9789813273238_0018","url":null,"abstract":"","PeriodicalId":259425,"journal":{"name":"Data Science and Knowledge Engineering for Sensing Decision Support","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127687877","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}
Á. Panizo, Gema Bello-Orgaz, A. Ortega, David Camacho
{"title":"Community finding in dynamic networks using a genetic algorithm improved via a hybrid immigrants scheme","authors":"Á. Panizo, Gema Bello-Orgaz, A. Ortega, David Camacho","doi":"10.1142/9789813273238_0076","DOIUrl":"https://doi.org/10.1142/9789813273238_0076","url":null,"abstract":"","PeriodicalId":259425,"journal":{"name":"Data Science and Knowledge Engineering for Sensing Decision Support","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127710395","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}
{"title":"Intelligent application of data fusion in garment manufacturing under the thinking of “Internet plus”","authors":"L. Cui, H. Dai, Kai Liu","doi":"10.1142/9789813273238_0185","DOIUrl":"https://doi.org/10.1142/9789813273238_0185","url":null,"abstract":"","PeriodicalId":259425,"journal":{"name":"Data Science and Knowledge Engineering for Sensing Decision Support","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132650046","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}
A. Grigorash, R. Bond, M. Mulvenna, S. O’neill, C. Armour, Colette Ramsey
{"title":"Frequency domain analysis of telephone helpline call data","authors":"A. Grigorash, R. Bond, M. Mulvenna, S. O’neill, C. Armour, Colette Ramsey","doi":"10.1142/9789813273238_0158","DOIUrl":"https://doi.org/10.1142/9789813273238_0158","url":null,"abstract":"The paper presents a frequency domain analysis of call data records of a telephone helpline for those seeking mental health and wellbeing support and for those who are in a suicidal crisis. A call data record dataset provided by Samaritans Ireland helpline is used. Fourier series is used to ascertain periodicity in the call volume. The main findings from the paper indicate that strong repetitive intra-day and intra-week patterns are found, while intra-month repetitions are conspicuously absent.","PeriodicalId":259425,"journal":{"name":"Data Science and Knowledge Engineering for Sensing Decision Support","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134511154","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}
{"title":"The empirical study of imported genetic algorithm combined with ant colony algorithm based on 3-SAT problems","authors":"Huimin Fu, Yang Xu, Xinran Ning, Wuyang Zhang","doi":"10.1142/9789813273238_0093","DOIUrl":"https://doi.org/10.1142/9789813273238_0093","url":null,"abstract":"","PeriodicalId":259425,"journal":{"name":"Data Science and Knowledge Engineering for Sensing Decision Support","volume":"112 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133847688","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}
{"title":"Computer aided diagnostic tool for prostate cancer with rule extraction from Support Vector Machines","authors":"Guanjin Wang, Jie Lu, J. Teoh, K. Choi","doi":"10.1142/9789813273238_0164","DOIUrl":"https://doi.org/10.1142/9789813273238_0164","url":null,"abstract":"Prostate cancer is a common malignancy among men, necessitating accurate and timely diagnosis at an early stage. With the advent of Artificial Intelligence (AI) technologies in the health field, support vector machines (SVMs) as one of the most well-known machine learning methods have been widely applied for prostate cancer detection. They have good generalization performances but no interpretability on the learned patterns, which bring difficulties for health professionals to understand the inner working of the predictive model. In this paper, we aim to build a computer aided diagnostic tool for prostate cancer using the SVMs where rule extraction is enabled. Experimental results on a real-world prostate cancer dataset collected in a Hong Kong hospital show that the proposed model not only had the ability for rule generation but also achieved better prediction results compared with decision tree, exhibiting a potential to assist physicians with clinical decision support in future.","PeriodicalId":259425,"journal":{"name":"Data Science and Knowledge Engineering for Sensing Decision Support","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134064622","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}
{"title":"A consensus model for large scale using hesitant information","authors":"Rosa M. Rodríguez, L. Martínez, G. Tré","doi":"10.1142/9789813273238_0027","DOIUrl":"https://doi.org/10.1142/9789813273238_0027","url":null,"abstract":"Nowadays due to the technological development, large-scale group decision making problems (LSGDM) are common and they often need to obtain accepted solutions for all experts involved in the problem. To do so, a consensus reaching process (CRP) is applied. A challenge in CRP for LSGDM is to overcome scalability problems. This paper presents a new consensus model to deal with LSGDM that is able to reduce the time cost of the CRP.","PeriodicalId":259425,"journal":{"name":"Data Science and Knowledge Engineering for Sensing Decision Support","volume":"100 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115539475","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}