2017 9th International Conference on Information and Knowledge Technology (IKT)最新文献_第3页

Augmented feature-state sensors in human activity recognition 增强特征状态传感器在人体活动识别中的应用

2017 9th International Conference on Information and Knowledge Technology (IKT) Pub Date : 2017-10-01 DOI: 10.1109/IKT.2017.8258620

M. Keyvanpour, Samaneh Zolfaghari

{"title":"Augmented feature-state sensors in human activity recognition","authors":"M. Keyvanpour, Samaneh Zolfaghari","doi":"10.1109/IKT.2017.8258620","DOIUrl":"https://doi.org/10.1109/IKT.2017.8258620","url":null,"abstract":"Nowadays, Human Activity Recognition (HAR) has gain a lot of interest because of demand growth in many applications particularly in smart homes as a fundamental task. This problem is typically addressed as a supervised learning problem with the goal of learning the mapping of extracted related features out of sensors data to the underlying human activities. Most of the proposed methods for HAR do not consider important information such as time domain features explicitly for activity modeling. In this paper, Augmented Feature-StAte (Statistical-Activity context) Sensors (AFSSs)are proposed to incorporate combination of important statistical features and activity context information. To evaluate the proposed AFSSs, they are applied in four benchmark and popular probabilistic graphical activity recognition algorithms including Naïve Bayesian classifiers (nBCs), Hidden Markov Models (HMMs), Hidden Semi Markov Models (HSMMs) and Linear-Chain Conditional Random Fields (LCCRFs). The experiments are performed on three well-known and real-world datasets in this field. The results show that the proposed AFSSs improve the classification performance particularly in terms of Fl-Score, accuracy and robustness.","PeriodicalId":338914,"journal":{"name":"2017 9th International Conference on Information and Knowledge Technology (IKT)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121949155","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 human factors affecting the acceptance of business intelligence us behavioral ing the behavioral model of reasoned action theory 影响商业智能接受度的人为因素是理性行为理论的行为模型

2017 9th International Conference on Information and Knowledge Technology (IKT) Pub Date : 2017-10-01 DOI: 10.1109/IKT.2017.8258619

S. Shahid, Rohollah Tavallaee, Solmaz Hosein Shobeiri

{"title":"The human factors affecting the acceptance of business intelligence us behavioral ing the behavioral model of reasoned action theory","authors":"S. Shahid, Rohollah Tavallaee, Solmaz Hosein Shobeiri","doi":"10.1109/IKT.2017.8258619","DOIUrl":"https://doi.org/10.1109/IKT.2017.8258619","url":null,"abstract":"In the world of today, business intelligence offers the possibility to convert data into useful intelligence and awareness for organizations, thereby enabling them to make prompt and prudent decisions. Factors affecting the adoption of business intelligence along with the assessment of their impact could, undoubtedly, be absolutely imperative for organizations. Among factors influencing the adoption of business intelligence, this paper discusses human factors that affect the adoption of business intelligence by taking advantage of a behavioral model of the reasoned action theory. In detail, a survey of 375 managers supported the proposed model. The Results demonstrated that that ideas associated with a person have a significant impact on behavioral attitude. To explain, Self-efficacy, awareness level, beliefs of the individual, subjective norms, and attitudes have a significant effect on intentionality. Furthermore, actual behaviour is significantly impacted by self-efficacy, awareness level, beliefs of the individual, and intentionality. However, awareness level and experience have no significant impact on behavioral attitude. Taking account of empirical findings, this study to present theoretical and management approaches for organizations.","PeriodicalId":338914,"journal":{"name":"2017 9th International Conference on Information and Knowledge Technology (IKT)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131136028","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

moLink: Modeling link representation of knowledge base moLink:知识库的链接表示建模

2017 9th International Conference on Information and Knowledge Technology (IKT) Pub Date : 2017-10-01 DOI: 10.1109/IKT.2017.8258613

S. Haghani, M. Keyvanpour

引用次数: 1

A new statistical detector for CT-based multiplicative image watermarking using the t location-scale distribution 一种新的基于ct的基于位置尺度分布的乘性图像水印统计检测器

2017 9th International Conference on Information and Knowledge Technology (IKT) Pub Date : 2017-10-01 DOI: 10.1109/IKT.2017.8258636

Sadegh Etemad, M. Amirmazlaghani

引用次数: 2

Protein complex detection from PPI networks on Apache Spark 在Apache Spark上从PPI网络中检测蛋白质复合物

2017 9th International Conference on Information and Knowledge Technology (IKT) Pub Date : 2017-10-01 DOI: 10.1109/IKT.2017.8258627

Mehdi Joodaki, Nasser Ghadiri, Amir Hossein Atashkar

{"title":"Protein complex detection from PPI networks on Apache Spark","authors":"Mehdi Joodaki, Nasser Ghadiri, Amir Hossein Atashkar","doi":"10.1109/IKT.2017.8258627","DOIUrl":"https://doi.org/10.1109/IKT.2017.8258627","url":null,"abstract":"Protein-Protein Interaction (PPI) network is a network of biomolecular interactions which plays a major role in modeling and analyzing biological activities. Studies of functional modules from PPI networks provide a better understanding of biological mechanisms. Recent advances in both biological and computer sciences demands for the vast amount of PPI networks data to be processed by experimental and computational methods. This could be a great challenge to find functional modules within these large networks. Existing methods are used to identify the functional modules, but some of them do not consider overlapping between functional module clusters. Moreover, most of the methods run on a single machine. Also, many existing algorithms only focus on topological features of PPI networks. In this paper, we introduce a new way for detecting the functional modules. It considers overlapping between clusters and runs on Apache Spark — a distributed processing platform. Our algorithm also considers both topological and biological features of PPI networks. The evaluation results show improved execution speed as well as more accurate results compared to classic methods.","PeriodicalId":338914,"journal":{"name":"2017 9th International Conference on Information and Knowledge Technology (IKT)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131510678","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: 5