in this paper, the notion of the digital divide has been described, and a few analyzing methods of digital divide have been reviewed. analyzing methods of digital divide are called indices which have different indicators and different formulas for calculation. since data collection for an indicator may be difficult, calculating an index is an essential problem. we collected and calculated some ...

this paper presents a fuzzy decision-making approach to deal with a clustering supplier problem in a supply chain system. during recent years, determining suitable suppliers in the supply chain has become a key strategic consideration. however, the nature of these decisions is usually complex and unstructured. in general, many quantitative and qualitative factors, such as quality, price, and fl...

Australian Business Deans Council (ABDC); Bacon’s Media Directory; Burrelle’s Media Directory; Cabell’s Directories; Compendex (Elsevier Engineering Index); CSA Illumina; Current

The growth in interest in RGB-D devices (e.g. Microsoft Kinect or ASUS Xtion Pro) is based on their low price, as well as the wide range of possible applications. These devices can provide skeletal data consisting of 3D position, as well as orientation data, which can be further used for pose or action recognition. Data for 15 or 20 joints can be retrieved, depending on the libraries used. Rece...

Data clustering is a process of arranging similar data into groups. A clustering algorithm partitions a data set into several groups such that the similarity within a group is better than among groups. In this paper a hybrid clustering algorithm based on K-mean and K-harmonic mean (KHM) is described. The proposed algorithm is tested on five different datasets. The research is focused on fast an...

K-means clustering algorithm is one of the most popular unsupervised learning algorithm that is broadly used to clustering the given data items. The k-means algorithm is one of the commonly used clustering methods in data mining. A number of algorithms have been developed for clustering the data items using K-Means due to its simplicity and efficiency. The final clustering result of the K-Means...