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This paper proposes the application of association rule mining to improve quizzes and courses. First, the paper shows how to preprocess quiz data and how to create several data matrices for use in the process of knowledge discovery. Next, the proposed algorithm that uses grammar-guided genetic programming is described and compared with both classical and recent soft-computing association rule mining algorithms. Then, different objective and subjective rule evaluation measures are used to select the most interesting and useful rules. Experiments have been carried out by using real data of university students enrolled on an artificial intelligence practice Moodle's course on the CLIPS programming language. Some examples of these rules are shown, together with the feedback that they provide to instructors making decisions about how to improve quizzes and courses. Finally, starting with the information provided by the rules, the CLIPS quiz and course have been updated. These innovations have been evaluated by comparing the performance achieved by students before and after applying the changes using one control group and two different experimental groups. [ABSTRACT FROM AUTHOR]

Case-based reasoning (CBR) has been used in various problem-solving areas such as financial forecasting, credit analysis and medical diagnosis. However, conventional CBR has the limitation that it has no criterion for choosing the nearest cases based on the probabilistic similarity of cases. It uses a fixed number of neighbors without considering an optimal number for each target case, so it does not guarantee optimal similar neighbors for various target cases. This leads to the weakness of lowering predictability due to deviation from desired similar neighbors. In this paper we suggest a new case extraction technique called statistical case-based reasoning. The main idea involves a dynamic adaptation of the optimal number of neighbors by considering the distribution of distances between potential similar neighbors for each target case. In order to do this, our technique finds the optimal distance threshold and selects similar neighbors satisfying the distance threshold criterion. We apply this new method to five real-life medical data sets and compare the results with those of the statistical method, logistic regression; we also compare the results with the learning methods C5.0, CART, neural networks and conventional CBR. The results of this paper show that the proposed technique outperforms those of many other methods, it overcomes the limitation of conventional CBR, and it provides improved classification accuracy. [ABSTRACT FROM AUTHOR]

To fully utilize the voltage regulation capacity and interaction characteristics of the Transmission and Distribution (T&D) system, a novel Modified Partially Observed Markov Decision Process (MPOMDP)‐based Reinforcement Learning (RL) scheme for Autonomous Voltage Control is proposed, which incorporates Demand Response (DR) and cooperation with the Transmission Network. The proposed scheme consists of two vital components: an MPOMDP block and a Modified Asynchronous Advantage Actor‐Critic‐based RL block. The MPOMDP block innovatively exploits the confidence interval of the observed state to make a better perception of the precise system state by introducing two new probability vectors. Then the MPOMDP block is fed into the underlying architecture of the RL block for asynchronously capturing features and optimal decision‐making, where the solving framework additionally brings in a public data buffer to realize boundary information sharing. Case studies are conducted on a modified T&D system considering N‐1 contingencies, with a training dataset from a district in Suzhou, China. Simulation results demonstrate that the proposed scheme can achieve significant voltage optimization while ensuring fast convergence speed. [ABSTRACT FROM AUTHOR]

This paper presents the development of an integrated system, namely the COMAND-DSS (Decision Support System created under project COMAND), for crisis management and real-time decision support in shipboard flooding emergencies. It employs the case-based reasoning approach to provide swift and effective decision-making support for shipboard personnel during an emergency on ship. The developed system, which focuses on decision support for flooding control, is also thought to be useful as a crew training system. In order to deal with a flooding damage in the COMAND-DSS, a multiple attribute decision-making methodology—Technique for Order Preference by Similarity to Ideal Solution (TOPSIS)—is used to be able to choose the set of counter-flooding tanks that should be filled in order to achieve an optimal response to a flooding accident. The COMAND-DSS is composed of a knowledge base, a reasoning process and a graphical user interface for the shipboard personnel to interact with the system. The friendly windows-based user interface of the COMAND-DSS enables the user to take full advantage of the capabilities of the system in order to make quick decisions. Using this system, when a crisis occurs the optimal decisions and responses can be made in a short time, thereby reducing the risk to life, property and environment. Copyright © 2006 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

A substantial progress in development of new and efficient tensor factorization techniques has led to an extensive research of their applicability in recommender systems field. Tensor-based recommender models push the boundaries of traditional collaborative filtering techniques by taking into account a multifaceted nature of real environments, which allows to produce more accurate, situational (e.g., context-aware and criteria-driven) recommendations. Despite the promising results, tensor-based methods are poorly covered in existing recommender systems surveys. This survey aims to complement previous works and provide a comprehensive overview on the subject. To the best of our knowledge, this is the first attempt to consolidate studies from various application domains, which helps to get a notion of the current state of the field. We also provide a high level discussion of the future perspectives and directions for further improvement of tensor-based recommendation systems. WIREs Data Mining Knowl Discov 2017, 7:e1201. doi: 10.1002/widm.1201 For further resources related to this article, please visit the . [ABSTRACT FROM AUTHOR]