There has been an increase in the amount of multilingual text on the Internet due to the proliferation of news sources and blogs. The Urdu language, in particular, has experienced explosive growth on the Web. Text mining for information discovery, which includes tasks such as identifying topics, relationships and events, and sentiment analysis, requires sophisticated natural language processing (NLP). NLP systems begin with modules such as word segmentation, part-of-speech tagging, and morphological analysis and progress to modules such as shallow parsing and named entity tagging. While there have been considerable advances in developing such comprehensive NLP systems for English, the work for Urdu is still in its infancy. The tasks of interest in Urdu NLP includes analyzing data sources such as blogs and comments to news articles to provide insight into social and human behavior. All of this requires a robust NLP system. The objective of this work is to develop an NLP infrastructure for Urdu that is customizable and capable of providing basic analysis on which more advanced information extraction tools can be built. This system assimilates resources from various online sources to facilitate improved named entity tagging and Urdu-to-English transliteration. The annotated data required to train the learning models used here is acquired by standardizing the currently limited resources available for Urdu. Techniques such as bootstrap learning and resource sharing from a syntactically similar language, Hindi, are explored to augment the available annotated Urdu data. Each of the new Urdu text processing modules has been integrated into a general text-mining platform. The evaluations performed demonstrate that the accuracies have either met or exceeded the state of the art.
PurposePublic‐private partnerships (PPPs) are being frequently used today to private sector investment in road projects. Most of the road PPP projects are either for new roads or for those that involve significant expansion of existing capacity. There are limited instances of PPPs for renovating and maintenance of existing roads. The purpose of this paper is to highlight the applicability of using PPPs for road renovation and maintenance projects.Design/methodology/approachThis paper uses a case‐study approach since it is an appropriate strategy to investigate a phenomenon within its real life context. The East Coast Road project was chosen for the study because it was the first project in India to use PPP for road renovation and maintenance, and being the first project of its kind, the case was of general public interest.FindingsThe paper indicates that risk levels in Rehabilitate, Improve, Maintain, Operate and Transfer (RIMOT) projects are lower than Greenfield BOT projects. Even in areas like renovation and maintenance, PPP structures can bring many advantages over traditional procurement.Research limitations/implicationsThis paper has the limitations attributable to single case studies. There is a need to extend this paper to include more such case studies to evaluate their relevance for infrastructure development, particularly in emerging countries.Practical implicationsPPP structures can be useful for renovating and maintaining the existing roads. Modalities such as the RIMOT framework can have greater potential than the conventional BOT structures. Private investments in infrastructure can also be through a corporate finance structure.Originality/valueThis paper describes and analyzes the experience of India's first PPP for renovation and maintenance. The findings of this paper would have value for policy makers who are interested in attracting private sector finance and expertise in infrastructure and more specifically in roads.
Often the challenge associated with tasks like fraud and spam detection is the lack of all likely patterns needed to train suitable supervised learning models. This problem accentuates when the fraudulent patterns are not only scarce, they also change over time. Change in fraudulent pattern is because fraudsters continue to innovate novel ways to circumvent measures put in place to prevent fraud. Limited data and continuously changing patterns makes learning signi cantly di cult. We hypothesize that good behavior does not change with time and data points representing good behavior have consistent spatial signature under di erent groupings. Based on this hypothesis we are proposing an approach that detects outliers in large data sets by assigning a consistency score to each data point using an ensemble of clustering methods. Our main contribution is proposing a novel method that can detect outliers in large datasets and is robust to changing patterns. We also argue that area under the ROC curve, although a commonly used metric to evaluate outlier detection methods is not the right metric. Since outlier detection problems have a skewed distribution of classes, precision-recall curves are better suited because precision compares false positives to true positives (outliers) rather than true negatives (inliers) and therefore is not a ected by the problem of class imbalance. We show empirically that area under the precision-recall curve is a better than ROC as an evaluation metric. The proposed approach is tested on the modi ed version of the Landsat satellite dataset, the modi ed version of the ann-thyroid dataset and a large real world credit card fraud detection dataset available through Kaggle where we show signi cant improvement over the baseline methods.
It is necessary to provide medication to the aged in time. Automatic medication dispenser is designed specifically for users who take medications without close professional supervision. It relieves the user of the error-prone tasks of administering wrong medicine at wrong time. The major components of this medication dispenser are a microcontroller interfaced with an alphanumeric keypad, an LED display, a Motor Controller, an Alarm system, a multiple pill container and dispenser. The overall operation is to facilitate the user to set the timings to dispense multiple pills at required timings. The Alarm system is designed to provide two types of indications -one by lighting an LED and the other by providing a beep sound. The user is required to press a button to get the pill and reset the alarm button. The second alarm is to indicate the optimal availability of the pills in the container to warn the user to refill the dispenser with the required quantity of pills.The major objective is to keep the device simple and cost efficient. The software used is reliable and stable. Elderly population can benefit from this device as it avoids expensive in-home medical care.
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