Energy simulation tool is a tool to simulate energy use by a building prior to the erection of the building. Commonly it has a feature providing alternative designs that are better than the user’s design. In this paper, we propose a novel method in searching alternative design that is by using classification method. The classifiers we use are Naïve Bayes, Decision Tree, and k-Nearest Neighbor. ...

This paper proposes a semi-supervised learning method using Fuzzy clustering to solve word sense disambiguation problems. Furthermore, we reduce side effects of semi-supervised learning by ensemble learning. We set classes for labeled instances. The -th labeled instance is used as the prototype of the -th class. By using Fuzzy clustering for unlabeled instances, prototypes are moved to more sui...

This paper introduces modifications of Self-Organizing Maps allowing imputation and classification of data containing missing values. The robustness of the proposed modifications is shown using experimental results of a standard data set. A comparison to modified Fuzzy cluster methods [Timm et al., 2002] is presented. Both methods performed better with available case analysis compared to comple...

The k nearest neighbor classification method is one of the humblest method in conceptually and it is a top method in image mining. In this work, the enhanced k nearest neighbor (EKNN) technique has been implemented to identify the cancer and automatic classification of benign and malignant tissues in the huge amount of lung cancer image datasets. In this proposed system, we have used three stag...

Nearest neighbor algorithms can be implemented on content-based image retrieval (CBIR) and classification problems for extracting similar images. In k-nearest neighbor (k-NN), the winning class is based on the k nearest neighbors determined by comparing the query image against all training samples. In this paper, a new nearest neighbor search (NNS) algorithm is proposed using a two-step process...

The moving k nearest neighbor query, which computes one’s k nearest neighbor set and maintains it while at move, is gaining importance due to the prevalent use of smart mobile devices such as smart phones. Safe region is a popular technique in processing the moving k nearest neighbor query. It is a region where the movement of the query object does not cause the current k nearest neighbor set t...

In this work, one and two-dimensional lattices are studied theoretically by a statistical mechanical approach. The nearest and next-nearest neighbor interactions are both taken into account, and the approximate thermodynamic properties of the lattices are calculated. The results of our calculations show that: (1) even though the next-nearest neighbor interaction may have an insignificant ef...

We present in this paper a simple, yet valuable improvement to the traditional k-Nearest Neighbor (kNN) classifier. It aims at addressing the issue of unbalanced classes by maximizing the class-wise classification accuracy. The proposed classifier also gives the option of favoring a particular class through evaluating a small set of fuzzy rules. When tested on a number of UCI datasets, the prop...

K-nearest neighbor (k-NN) classification is a powerful and simple method for classification. k-NN classifiers approximate a Bayesian classifier for a large number of data samples. The accuracy of k-NN classifier relies on the distance metric used for calculating nearest neighbor and features used for instances in training and testing data. In this paper we use deep neural networks (DNNs) as a f...

To have high performance scheduling mechanisms in grid computing, we need accurate methods for estimating parameters like jobs' wait time and run time. In this paper, we consider wait time prediction problem. Different regression techniques are examined on AuverGrid data set to predict wait time. To improve the quality of prediction, some extra features are proposed. Simulation results show tha...