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.

Q: 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.

A: NeuroSVM: A Graphical User Interface for Identification of Liver Patients
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Q: As the education fees are becoming more expensive, more students apply for scholarships. Consequently, hundreds and even thousands of applications need to be handled by the sponsor. To solve the problems, some alternatives based on several attributes (criteria) need to be selected. In order to make a decision on such fuzzy problems, Fuzzy Multiple Attribute Decision Making (FMDAM) can be applied. In this study, Unified Modeling Language (UML) in FMADM with TOPSIS and Weighted Product (WP) methods is applied to select the candidates for academic and non-academic scholarships at Universitas Islam Negeri Sunan Kalijaga. Data used were a crisp and fuzzy data. The results show that TOPSIS and Weighted Product FMADM methods can be used to select the most suitable candidates to receive the scholarships since the preference values applied in this method can show applicants with the highest eligibility. Keyword: Fuzzy Multiple Attribute Decision Making, TOPSIS, Weighted Product, Scholarship

A: A Fuzzy Topsis Multiple-Attribute Decision Making for Scholarship Selection
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Q: <lb>In multi-agent domains, an agent’s action may not just change the world and the agent’s knowledge and beliefs<lb>about the world, but also may change other agents’ knowledge and beliefs about the world and their knowledge and<lb>beliefs about other agents’ knowledge and beliefs about the world. Similarly, the goals of an agent in a multi-agent<lb>world may involve manipulating the knowledge and beliefs of other agents’ and again, not just their knowledge<lb>about the world, but also their knowledge about other agents’ knowledge about the world. The goal of this paper is to<lb>present an action language, called mA+, that has the necessary features to address the above aspects in representing<lb>and reasoning about actions and change in multi-agent domains.<lb>This action language can be viewed as a generalization of the single-agent action languages extensively studied<lb>in the literature, to the case of multi-agent domains. The language allows the representation of and reasoning about<lb>different types of actions that an agent can perform in a domain where many other agents might be present—such as<lb>world-altering actions, sensing actions, and announcement/communication actions. The action language also allows<lb>the specification of agents’ dynamic awareness of action occurrences which has future implications on what agents’<lb>know about the world and other agents’ knowledge about the world. The language mA+ considers three different<lb>types of awareness: full awareness, partial awareness, and complete oblivion of an action occurrence and its effects.<lb>This keeps the language simple, yet powerful enough to address a large variety of knowledge manipulation scenarios<lb>in multi-agent domains.<lb>The semantics of the language relies on the notion of state, which is described by a pointed Kripke model and<lb>is used to encode the agent’s knowledge and the real state of the world. The semantics is defined by a transition<lb>function that maps pairs of actions and states into sets of states. The paper illustrates properties of the action theories,<lb>including properties that guarantee finiteness of the set of initial states and their practical implementability. Finally,<lb>the paper relates mA+ to other related formalisms that contribute to reasoning about actions in multi-agent domains.

A:
An Action Language for Multi-Agent Domains: Foundations
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