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Complicated sequential images acquired from the automatic particle inspection machine are used to extract tiny objects within bottled medical liquid on pharmaceutical production line. We propose a learning-based inspection method based on the theory of sparse representation and dictionary learning, which converts the inspection problem into background modeling. As discussed in the paper, the way of...
Group Decision Making (GDM) is a usual process in companies and administration in which complex decision problems are solved taking into account different points of view from different experts involved in the decision situation. Notwithstanding, in principle group decisions should be better accepted than decisions made by a single decision maker because they try to include several viewpoints, sometimes...
The induction of classifiers by means of supervised learning techniques is one of the most common and extended applications in the field of the intelligent systems. Multi-classifier systems obtain a set of basic classifiers and uses it to predict the class of a data instance. In this work, a new method to reduce a set of classifiers to their equivalent minimal set is presented. For this purpose, a...
To improve the precision and generalization of ensemble model and leaching model, a novel selective hierarchical ensemble modeling approach is proposed for leaching rate prediction in this paper. Unlike previous selective ensemble model, the new selective ensemble model is a hierarchical model. The model considers not only the combination of sub-models, but also the generation of sub-models. First...
Recently, Deep Belief Networks (DBNs) have received much attention in speech recognition communities. However, there are rare methods to set the appropriate hidden layers of DBNs. In this paper, we study the relationship between the number of hidden layers and the invariant features of speech signals, and the time cost of the accuracy of speech recognition. Also, we study the approximations in Contrastive...
Clustering ensemble takes advantage of ensemble learning technique, combining multiple cluster members' results to get uniform and more reasonable clustering result. This paper integrates in the staged results of spectral partition ensemble algorithm orderly, applying the fuzzy C-means clustering algorithm in the following clustering stage of spectral partition ensemble, and presents four fuzzy spectral...
Recommender systems typically use collaborative filtering to make sense of huge and growing volumes of data. However, sharing user-item preferential data for use in collaborative filtering poses significant privacy and security challenges. In recent years, privacy has attracted a lot of attention. There are many existing works on privacy-preserving collaborative filtering. However, while these schemes...
A new ranking methodology for general interval type 2 fuzzy sets (IT2FS) using extended alpha cuts representation is proposed. First, the extended alpha cut representation of the IT2FS is defined. Then, the parametric representation of the type 1 fuzzy sets (T1FS) is formulated by constructing the embedded pessimistic, modal and optimistic functionals. The corresponding parametric centroid functional...
Aiming to improve the efficiency of alpha-paramodulation in lattice-valued logic with equality, this paper focuses on alpha-lock paramodulation for lattice-valued logic, which can more efficiently handle the lattice-valued logical formula with equality. The definition of alpha-lock paramodulation is given firstly, which is a refinement of alpha-paramodulation, and then its soundness and completeness...
In this paper, a system methodology evaluating model designed for estimating the urban traffic condition trend is raised under the circumstance of specific traffic policy. Additionally, the conceptual understanding of system dynamics and its application in traffic is raised in detail. Furthermore, a specific example is offered to make model verification while indicating the accuracy of the whole system...
Bag-of-Words representation based on visual words has been approved to be used widely in scene classification. Visual words are usually constructed by using SIFT(Scale Invariant Feature Transform) of patches. Traditional SIFT descriptor is limited in describe the outdoor scene completely and accurately because it does not consider the multi-directional context and global color information of image...
Sensors are a common way of collecting health data on any sophisticated machinery. Utilizing the real time machine sensor data in order to predict problems ahead in time to mitigate the risk associated with unplanned failures has been of great interest to statisticians and reliability engineers as there are direct cost saving benefits. In effort to reduce downtime and improve overall reliability of...
Topic model has been used to extract implicit features yet little concerns have been given to general opinion words, e.g., "Okey" (good). In this paper we present a modified topic model joint topic-opinion model (JTO) for extracting implicit features of opinion words including special and general ones. Our model is based on an extension to standard LDA model by adding an opinion level. This...
Global competition, ever shorter product life cycles and pressure from environmental protection are forcing companies to explore more agile and sustainable supply chain practices. Motivated by these needs, this paper aims to study how to achieve agility in sustainable supply chains by exploiting the state of the art information technologies. Our goal is to develop architecture and propose a solution...
Sparse matrix-vector multiplication (SpMV) is a memory intensive kernel, executing with different thread quantity is very different in performance. In this paper, we present a new method based on knowledge discovery technology to give the optimal thread quantity to improve SpMV performance and reduce execution time. Considering the feature of sparse matrix, which affecting the efficiency of SpMV,...
In this article, we focus on group decision making when preferences are expressed through the LCP-nets (Linguistic Conditional Preference networks). There is a need for complementing efforts and research on mixing LCP-nets together to be able to compute group preferences. First steps are the comparison between two LCP-nets and the aggregation of LCP-nets. In order to compare then aggregate two LCP-nets,...
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