Dispelling Classes Gradually to Improve Quality of Feature Reduction Approaches

Feature reduction is an important concept which is used for reducing dimensions to decrease the computation complexity and time of classification. Since now many approaches have been proposed for solving this problem, but almost all of them just presented a fix output for each input dataset that some of them aren't satisfied cases for classification. In this we proposed an approach as processing input dataset to increase accuracy rate of each feature extraction methods. First of all, a new concept called dispelling classes gradually (DCG) is proposed to increase separability of classes based on their labels. Next, this method is used to process input dataset of the feature reduction approaches to decrease the misclassification error rate of their outputs more than when output is achieved without any processing. In addition our method has a good quality to collate with noise based on adapting dataset with feature reduction approaches. In the result part, two conditions (With process and without that) are compared to support our idea by using some of UCI datasets.Comment: 11 Pages, 5 Figure, 7 Tables; Advanced Computing: An International Journal (ACIJ), Vol.3, No.3, May 201

Agricultural Trade Liberalisation and Economic Growth in Developing Countries: Analysis of Distributional Consequences

The article analyses the impact of agricultural trade liberalisation on economic growth as well as on the welfare of rural livelihoods in developing countries through technological transformation in the agricultural sector. The article, based on existing literature, considers the background and reasons for the policy shift in developing economies away from agricultural protection and toward trade liberalisation. It attempts to shed light on the debate over the distributional consequences resulting from trade liberalisation. It also analyses how agricultural trade policy reforms affect poverty and inequality, since the majority of the population of developing countries is involved with agriculture, and these households are predominantly rural poor and functionally landless. The study found that trade liberalisation in the agricultural sector has had positive impacts on the agricultural sector but has contributed very little to poverty reduction because of the lack of income distribution and inequality measures in the policy sphere. The article might be useful for policy makers and researchers.agriculture, developing countries, growth, inequality, trade liberalisation, Agribusiness, Agricultural and Food Policy, Agricultural Finance, Community/Rural/Urban Development, Crop Production/Industries, Farm Management, Institutional and Behavioral Economics, International Development, Labor and Human Capital, Land Economics/Use, Political Economy, Research and Development/Tech Change/Emerging Technologies,

Generative Mixture of Networks

A generative model based on training deep architectures is proposed. The model consists of K networks that are trained together to learn the underlying distribution of a given data set. The process starts with dividing the input data into K clusters and feeding each of them into a separate network. After few iterations of training networks separately, we use an EM-like algorithm to train the networks together and update the clusters of the data. We call this model Mixture of Networks. The provided model is a platform that can be used for any deep structure and be trained by any conventional objective function for distribution modeling. As the components of the model are neural networks, it has high capability in characterizing complicated data distributions as well as clustering data. We apply the algorithm on MNIST hand-written digits and Yale face datasets. We also demonstrate the clustering ability of the model using some real-world and toy examples.Comment: 9 page

Impact of part time work on the academic performance of international students / Ershad Ali

The study analyses the impact of part time work on academic performance of international students while they study. In doing so, the study has conducted a survey among international students who were studying at different tertiary institutes in Auckland region. The study found that there are positive as well as negative impacts on the students’ academic performance while they study as well as work. The study opines that whether the impact would be positive or negative depends on time management between work and study. Findings of the study may be of interest for policy makers, educationists, and researchers

Gabor Barcodes for Medical Image Retrieval

In recent years, advances in medical imaging have led to the emergence of massive databases, containing images from a diverse range of modalities. This has significantly heightened the need for automated annotation of the images on one side, and fast and memory-efficient content-based image retrieval systems on the other side. Binary descriptors have recently gained more attention as a potential vehicle to achieve these goals. One of the recently introduced binary descriptors for tagging of medical images are Radon barcodes (RBCs) that are driven from Radon transform via local thresholding. Gabor transform is also a powerful transform to extract texture-based information. Gabor features have exhibited robustness against rotation, scale, and also photometric disturbances, such as illumination changes and image noise in many applications. This paper introduces Gabor Barcodes (GBCs), as a novel framework for the image annotation. To find the most discriminative GBC for a given query image, the effects of employing Gabor filters with different parameters, i.e., different sets of scales and orientations, are investigated, resulting in different barcode lengths and retrieval performances. The proposed method has been evaluated on the IRMA dataset with 193 classes comprising of 12,677 x-ray images for indexing, and 1,733 x-rays images for testing. A total error score as low as $351$ ($\approx 80\%$ accuracy for the first hit) was achieved.Comment: To appear in proceedings of The 2016 IEEE International Conference on Image Processing (ICIP 2016), Sep 25-28, 2016, Phoenix, Arizona, US