Volume 2 Issue 2

Authors: N.S.T. Sai; R.C. Patil

Abstract: In this paper we present a new method for Content Based Image Retrieval (CBIR). Image signature computed by using the Singular Value Decomposition (SVD). Singular values used as a feature are obtained from SVD of full image and sub block of image with different color spaces. Seven color spaces are used for the proposed method. Singular values for the feature vectors are 8,16,32,64 and 200 for the full image and it is different for block based SVD. For block based SVD image we use 8x8, 16x16, 32x32, 64x64 and 128x128 sub blocks to calculate feature vector. So we can compare the result of different color with full and block based SVD. Similarity between the query image and database image measured here by using simple Euclidean distance (ED) and Bray Curtis distance (BCD). The average precision and average recall of each image category and overall average precision and overall average recall is considered for the performance measure. Proposed method tested on the database include 1200 images has 15 different classes to compare the performance.

Keywords: CBIR; SVD; RGB; YCbCr; YUV; CXY; R’G’I; HSV; Precision; Recall; Euclidean Distance; Bray Curtis Distance

Doi:10.5963/IJCSAI0202002

Authors: M. Khashei; S.M. Manzari; F. Mokhatab Rafiei

Abstract: The disturbance incidences are common events in the flight networks in many countries. On the other hand, the survival of busy airlines in this strict and competitive business is crucial and tough. Therefore solving flight perturbation scheduling problem, in order to provide an optimized schedule in low computational time, is very important. This problem and its related literature have not yet been investigated seriously in Iran. One of the smart methods in solving routing and scheduling problems is ant colony optimization method which creates high quality results. In this paper, a model and a number of solution strategies for flight perturbation problem are presented. An approach based on ant colony optimization algorithm is used to solve the model. The computational results with real problem data show that the proposed method is highly efficient and effective in solving complex perturbation problems. The use of the proposed method can increase fleet life time and decrease air traffic and fuel consumption.

Keywords: Flight Perturbation Scheduling; Smart Optimization; Ant Colony Meta-Heuristic Algorithm

Doi:10.5963/IJCSAI0202001