An efficient recommender system algorithm using trust data

The Journal of Supercomputing - Smart services are a concept that provides services to the citizens in an efficient manner. The online shopping and recommender system can play an important role for...     

Uncertain clustering algorithms based on rough and fuzzy sets for real-time image segmentation

In real pattern recognition applications, the complete and accurate information of a given set is not always easy to get. Such incomplete knowledge may lead to imperfect expressions of the set using many pattern recognition methods. Rough sets theory is designed to approximately describe an imprecise set by a pair of lower and upper approximations which are weighted by different parameters. As the distributive character varies from one set to another, it is undesirable to employ a constant weighted parameter for controlling the importance of the lower and upper approximations on describing various given sets. This paper presents an improved rough-fuzzy c-means clustering algorithm in which a parameter selection strategy is designed to adaptively adjust the weighted parameter depending on the distributive character of each cluster instead of manually choosing a constant parameter. Such an online-decision method enables the formed prototype to get close to the desirable location. Experimental results on synthetic datasets, real-life datasets, and image segmentation problems confirm the effectiveness of the proposed adaptive parameter selection strategy. With the introduction of adaptive parameter selection strategy, the improved rough sets-based clustering algorithm outperforms its counterparts in certain cases.     

Group affinity based social trust model for an intelligent movie recommender system

As many researchers have taken an interest in social networks with the development of the user-generated web, trust management and its application have come into the spotlight. User information that is extracted by behavior patterns and user profiles provides the essential relationship between individuals. In this paper, we propose an intelligent movie recommender system with a social trust model. The proposed system is based on a social network for analyzing social relationships between users and generated group affinity values with user profiles. In experiments, the performance of this system is evaluated with precision-recall and F-measures.     

Dynamic knowledge management from multiple sources in crowdsourcing environments

Due to the spread of smart devices and the development of network technology, a large number of people can now easily utilize the web for acquiring information and various services. Further, collective intelligence has emerged as a core player in the evolution of technology in web 2.0 generation. It means that people who are interested in a specific domain of knowledge can not only make use of the information, but they can also participate in the knowledge production processes. Since a large volume of knowledge is produced by multiple contributors, it is important to integrate and manage knowledge efficiently. In this paper, we propose a social tagging-based dynamic knowledge management system in crowdsourcing environments. The approach here is to categorize and package knowledge from multiple sources, in such a way that it easily links to target knowledge.     

A Blockchain-Based Trust Model for Supporting Collaborative Healthcare Data Management

The development of information technology allows the collaborative business process to be run across multiple enterprises in a larger market environment. However, while collaborative business expands the realm of businesses, it also causes various hazards in collaborative Interaction, such as data falsification, inconstancy, and misuse. To solve these issues, a blockchain-based collaborative business modeling approach was proposed and analyzed. However, the existing studies lack the blockchain risk problem-solving specification, and there is no verification technique to examine the process. Consequently, it is difficult to confirm the appropriateness of the approach. Thus, here, we propose and build a blockchain-based trust model to strengthen and verify the integrity and security of the collaborative business process; Integrity and security address the validity of collaborative interactions in terms of a trust, and we construct a blockchain pattern based on trust elements to meet the required the characteristics. Specifically, a trust model can be applied to the healthcare data-sharing process, and then the achievement of the trust-based safe data-sharing process can be proven. Our model can be used as a trust-building guidance tool or for integrity and security verification with the collaborative business process in a distributed environment with blockchain.     

Real-time imaging-based assessment model for improving teaching performance and student experience in e-learning

Multimedia is an essential and integral part of electronic learning (e-learning). In this study, teaching performance and student learning experience are measured using real-time multimedia processing tools and techniques for the e-learning paradigm. Visual attention and visual engagement analysis are performed using two developed algorithms. Video lectures are recorded and delivered to students in e-learning pedagogical setup, which are examined for the visual attention and visual engagement of the student and teacher, respectively. Proposed methodology integrates the assessment on both student and teacher ends. Multimedia processing of video lectures for teaching performance produces scoring dataset. The same methodology on student end for visual attention is used to investigate student experience. These types of datasets then reduced to time-based datasets from the image-based dataset. Correlation and association of both datasets provide the opportunity to relate both student experience and teaching performance as well as to move forward to create content that is more useful. Computational performance of the developed algorithms is compared using different video lectures with their processed frames per second, which is analyzed as per their corresponding bins. Mean, max, and median of the processed frames of all the processed videos are also compared.