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Many recommendation systems recommend item (book, music, news, or restaurant) to a group of users with the help of social network services like micro-blogging, which are called as group recommendation systems. However, as the social network services always contain large amounts of information on different topics, and people have different influence on different topic, so the group recommendation systems...
This paper reports the performance analysis of a proposed auto-regressive (AR) model-based linear predictor algorithm with Kalman filtering (KF). The relationship between the optimum AR order and the channel correlation coefficient is investigated by means of the Akaike Information Criterion (AIC). Through our analysis, 2nd-order differential model based on the AR model-based linear predictor algorithm...
Many problems in the real world are, in general modeled as binary classification problems and often one class samples outnumber other class samples. This imbalance causes the reduction in accuracy of prediction in minority class samples but give overall high accuracy. Ignoring misclassification rate of minority class causes severe problems in many cases such as fraudulent credit card transactions,...
In this paper we examine an advanced collaborative filtering method that uses similarity transitivity concepts. By propagating "similarity" between users, in a similar way as with "trust", we can significantly expand the space of potential recommenders and system's coverage, improving also the recommendations' accuracy. While "trust" information might be missing or be...
Approaches to detect fault-prone modules have been studied for a long time. As one of these approaches, we proposed a technique using a text filtering technique. We assume that bugs relate to words and context that are contained in a software module. Our technique treats a module as text information. Based on the dictionary which was learned by classifying modules which induce bugs, the bug inducing...
Link prediction is a fundamental problem in social network analysis. The key technique in link prediction is to find an appropriate similarity measure between nodes of a network. Generally, external information besides the network topology is considered in many similarity measures. However, these external information is generally not available or not true. Usage of these external information may result...
A lot of manifold learning algorithms have been developed, which are used to learn a low dimensional model on a manifold representing large numbers of data in high dimensionality. Multi-manifold learning algorithms have also been put forward to provide a compact representation when these data come from different classes, with different intrinsic dimensionalities. However, when unseen data samples...
Image-based kinship recognition is an important problem in the reconstruction and analysis of social networks. Prior studies on image-based kinship recognition have focused solely on pair wise kinship verification, i.e. on the question of whether or not two people are kin. Such approaches fail to exploit the fact that many real-world photographs contain several family members, for instance, the probability...
Particle swarm optimization (PSO) algorithm has attracted great attention as a stochastic optimizing method due to its simplicity and power strength in optimization fields. However, two issues are still to be improved, especially, for complex multimodal problems. One is the premature convergence for multimodal problems. The other is the low efficiency for complex problems. To address these two issues,...
The ability to predict the career of students can be beneficial in a huge number of different techniques which are connected with the education structure. Student's marks and the result of some kind of psychometric test on students can form the training set for the supervised data mining algorithms. As the student's data in the educational systems is increasing day by day, the incremental learning...
With the development of personalized recommendation, the method of user interest prediction has been a hot research topic. Usually, predict methods use individual related parameters such as user ratings to infer possible user interests. A potential problem with these methods is that the credibility of the user ratings is rarely questioned or considered during the process of prediction. However, as...
In this paper, the hybrid method is proposed for sensor data predicting in the Internet of Things which combining the Ensemble Empirical Mode Decomposition (EEMD), Support Vector Regression (SVR), Particle Swarm Optimization (PSO) algorithm. The proposed hybrid method is examined by several kinds of sensor data. The obtained results confirm the universality, generality and high forecasting accuracy...
Nowadays, processing traffic flows has become an important part in intelligent transportation system (ITS). Prediction and estimation of flows, as a main application in this field, has gradually developed. Moreover, there exist some inherent relationships among various traffic flows, and the mining of related information can provide a platform for traffic flow prediction and estimation, and it can...
We introduce a multiple instance learning algorithm based on randomized decision trees. Our model extends an existing algorithm by Bloc keel et al. [2] in several ways: 1) We learn a random forest instead of a single tree. 2) We construct the trees by splits based on non-linear boundaries on multiple features at a time. 3) We learn an optimal way of combining the decisions of multiple trees under...
Using ensemble of classifiers on sequential chunks of training instances is a popular strategy for data stream mining. Aiming at the limitations of the existing approaches, we introduce recalling and forgetting mechanisms into ensemble based data stream mining, and put forward a new algorithm MAE (Memorizing based Adaptive Ensemble) to mine chunk-based data streams with concept drifts. Ensemble pruning...
We present a novel, real time algorithm to compute the trajectory of each pedestrian in moderately dense crowd scenes. Our formulation is based on an adaptive particle filtering scheme that uses a multi-agent motion model based on velocity-obstacles, and takes into account local interactions as well as physical and personal constraints of each pedestrian. Our method dynamically changes the number...
Link prediction is a fundamental task for analyzing complex networks which has been widely used in many domains, such as, identify spurious interactions, extract missing information, evaluate complex network evolving mechanism. There exist a variety of techniques for link prediction, ranging from node similarity-based methods to probabilistic graphical models. Node similarity-based methods have low...
Transfer learning aims to address the problem where we lack the labeled data for training in one domain while utilizing the sufficient training data from other relevant domains. The problem becomes even more challenging when there are no labeled data in the target domain to build the association between two domains, which is more common in real-world scenarios. In this paper, we tackle with the challenge...
Deep Brain Stimulation (DBS) is a surgical procedure to treat some progressive neurological movement disorders, such as Essential Tremor (ET), in an advanced stage. Current FDA-approved DBS systems operate open-loop, i.e., their parameters are unchanged over time. This work develops a Decision Tree (DT) based algorithm that, by using non-invasively measured surface EMG and accelerometer signals as...
Elasticity is one of the key benefits of cloud computing which helps customers reduce the cost. Although elasticity is beneficiary in terms of cost, obligation of maintaining Service Level Agreements leads to necessity in dealing with the cost-performance trade-off. Proactive auto-scaling is an efficient approach to overcome this problem. In this approach scaling actions are generated based on prediction...
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