In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.

Diagnosis of liver infection at preliminary stage is important for better treatment. In today’s scenario devices like sensors are used for detection of infections. Accurate classification techniques are required for automatic identification of disease samples. In this context, this study utilizes data mining approaches for classification of liver patients from healthy individuals. Four algorithms (Naïve Bayes, Bagging, Random forest and SVM) were implemented for classification using R platform. Further to improve the accuracy of classification a hybrid NeuroSVM model was developed using SVM and feed-forward artificial neural network (ANN). The hybrid model was tested for its performance using statistical parameters like root mean square error (RMSE) and mean absolute percentage error (MAPE). The model resulted in a prediction accuracy of 98.83%. The results suggested that development of hybrid model improved the accuracy of prediction. To serve the medicinal community for prediction of liver disease among patients, a graphical user interface (GUI) has been developed using R. The GUI is deployed as a package in local repository of R platform for users to perform prediction.
NeuroSVM: A Graphical User Interface for Identification of Liver Patients

We study truthful mechanisms for matching and related problems in a partial information<lb>setting, where the agents’ true utilities are hidden, and the algorithm only has access to ordi-<lb>nal preference information. Our model is motivated by the fact that in many settings, agents<lb>cannot express the numerical values of their utility for different outcomes, but are still able<lb>to rank the outcomes in their order of preference. Specifically, we study problems where the<lb>ground truth exists in the form of a weighted graph of agent utilities, but the algorithm can<lb>only elicit the agents’ private information in the form of a preference ordering for each agent<lb>induced by the underlying weights. Against this backdrop, we design truthful algorithms to<lb>approximate the true optimum solution with respect to the hidden weights. Our techniques<lb>yield universally truthful algorithms for a number of graph problems: a 1.76-approximation<lb>algorithm for Max-Weight Matching, 2-approximation algorithm for Max k-matching, a 6-<lb>approximation algorithm for Densest k-subgraph, and a 2-approximation algorithm for Max<lb>Traveling Salesman as long as the hidden weights constitute a metric. We also provide im-<lb>proved approximation algorithms for such problems when the agents are not able to lie about<lb>their preferences. Our results are the first non-trivial truthful approximation algorithms for<lb>these problems, and indicate that in many situations, we can design robust algorithms even<lb>when the agents may lie and only provide ordinal information instead of precise utilities.
Truthful Mechanisms for Matching and Clustering in an Ordinal World

We present a new algorithm to model and investigate the learning process of a learner mastering a set of grammatical rules from an inconsistent source. The compelling interest of human language acquisition is that the learning succeeds in virtually every case, despite the fact that the input data are formally inadequate to explain the success of learning. Our model explains how a learner can successfully learn from or even surpass its imperfect source without possessing any additional biases or constraints about the types of patterns that exist in the language. We use the data collected by Singleton & Newport (2004) on the performance of a 7-year boy Simon, who mastered the American Sign Language (ASL) by learning it from his parents, both of whom were imperfect speakers of ASL. We show that the algorithm possesses a frequency-boosting property, whereby the frequency of the most common form of the source is increased by the learner. We also explain several key features of Simon’s ASL.

When learners surpass their models: mathematical modeling of learning from an inconsistent source