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Not AvailableAlthough India has progressed significantly on several health outcomes but the state of food and nutrition security in the country still requires sustained efforts to accelerate achievement. Existing data based on socio-economic surveys conducted by National Sample Survey Office (NSSO) produce precise measures of food and nutrition security status at state and national level. However, these surveys cannot be used directly to produce reliable district or further smaller domain level estimates because of small sample sizes which lead to high level of sampling variability. Decentralized administrative planning system in India demands the availability of disaggregate (e.g. district) level statistics for target oriented effective policy planning and monitoring, as food and nutrition security is often unevenly distributed among the subsets of relatively small areas. But, due to lack of district level estimates, the mapping and analyse related to food and nutrition security measures are restricted to state and national level. As a result, disaggregate level dissimilarity and variability existing in food and nutrition security are often masked. This article delineates multivariate small area estimation (SAE) technique to obtain reliable and representative estimates of food consumption and nutrition status at district level for the rural areas of state of Uttar Pradesh in India by combining latest round of available Household Consumer Expenditure Survey 2011–2012 data of NSSO and the Indian Population Census 2011. The empirical evidence indicate that the estimates generated by SAE approach are reliable and representative. Spatial maps showing district level inequality in distribution of food and nutrition security in Uttar Pradesh is also produced. The disaggregate level estimates and spatial maps of food and nutrition security are directly relevant to sustainable development goal indicator 2.1.2—severity of food insecurity. The estimates and maps of food insecurity indictors are anticipated to offer irreplaceable information to administrative decisionmakers and policy experts for identifying the regions requiring more attention. Government of India has recently launched number of schemes for the benefit of rural population in the country and these estimates will be useful for fund allocation as well as in the monitoring of these schemesNot Availabl

Active Learning for Hierarchical Text Classification

Abstract. Hierarchical text classification plays an important role in many real-world applications, such as webpage topic classification, product categorization and user feedback classification. Usually a large numberoftrainingexamplesare neededtobuildanaccurate hierarchical classification system. Active learning has been shown to reduce the training examples significantly, but it has not been applied to hierarchical text classification due to several technical challenges. In this paper, we study active learning for hierarchical text classification. We propose a realistic multi-oracle setting as well as a novel active learning framework, and devise several novel leveraging strategies under this new framework. Hierarchical relation between different categories has been explored and leveraged to improve active learning further. Experiments show that our methods are quite effective in reducing the number of oracle queries (by 74 % to 90%) in building accurate hierarchical classification systems. As far as we know, this is the first work that studies active learning in hierarchical text classification with promising results.

Two-phase layered learning recommendation via category structure

Context and social network information have been introduced to improve recommendation systems. However, most existing work still models users’ rating for every item directly. This approach has two disadvantages: high cost for handling large amount of items and unable to handle the dynamic update of items. Generally, items are classified into many categories. Items in the same category have similar/relevant content, and hence may attract users of the same interest. These characteristics determine that we can utilize the item’s content similarity to overcome the difficultiess of large amount and dynamic update of items. In this paper, aiming at fusing the category structure, we propose a novel two-phase layered learning recommendation framework, which is matrix factorization approach and can be seen as a greedy layer-wise training: first learn user’s average rating to every category, and then, based on this, learn more accurate estimates of user’s rating for individual item with content and social relation ensembled. Based on two kinds of classifications, we design two layered gradient algorithms in our framework. Systematic experiments on real data demonstrate that our algorithms outperform other state-of-the-art methods, especially for recommending new items.Ke Ji, Hong Shen, Hui Tian, Yanbo Wu, Jun W

A Pattern Recognition Approach for Peak Prediction of Electrical Consumption

Part 6: Classification Pattern RecognitionInternational audiencePredicting and mitigating demand peaks in electrical networks has become a prevalent research topic. Demand peaks pose a particular challenge to energy companies because these are difficult to foresee and require the net to support abnormally high consumption levels. In smart energy grids, time-differentiated pricing policies that increase the energy cost for the consumers during peak periods, and load balancing are examples of simple techniques for peak regulation. In this paper, we tackle the task of predicting power peaks prior to their actual occurrence in the context of a pilot Norwegian smart grid network.While most legacy studies formulate the problem as time-series-based estimation problem, we take a radically different approach and map it to a classical pattern recognition problem using a simple but subtle formulations. Among the key findings of this study is the ability of the algorithms to accurately detect 80% of energy consumption peaks up to one week ahead of time. Further, different classification methods have been rigorously tested and applied on real-life data from a Norwegian smart grid pilot project

Automatic Text Summarization with Genetic Algorithm-Based Attribute Selection

The task of automatic text summarization consists of generating a summary of the original text that allows the user to obtain the main pieces of information available in that text, but with a much shorter reading time. This is an increasingly important task in the current era of information overload. given the huge amount of text available in documents. In this paper the automatic text summarization is cast as a classification (supervised learning) problem, so that machine learning-oriented classification methods are used to produce summaries for documents based on a set of attributes describing those documents. The goal of the paper is to investigate the effectiveness of Genetic Algorithm (GA)-based attribute selection in improving the performance of classification algorithms solving the automatic text summarization task. Computational results are reported for experiments with a document base formed by news extracted from The Wall Street Journal of the TIPSTER collection-a collection that is often used as a benchmark in the text summarization literature