Journal• ISSN: 0883-9514
Applied Artificial Intelligence
Taylor & Francis
About: Applied Artificial Intelligence is an academic journal published by Taylor & Francis. The journal publishes majorly in the area(s): Computer science & Artificial neural network. It has an ISSN identifier of 0883-9514. Over the lifetime, 1588 publications have been published receiving 33401 citations. The journal is also known as: AAI.
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Papers
TL;DR: This analysis indicates that missing data imputation based on the k-nearest neighbor algorithm can outperform the internal methods used by C4.5 and CN2 to treat missing data, and can also outperforms the mean or mode imputation method, which is a method broadly used to treatMissing values.
Abstract: One relevant problem in data quality is missing data. Despite the frequent occurrence and the relevance of the missing data problem, many machine learning algorithms handle missing data in a rather naive way. However, missing data treatment should be carefully treated, otherwise bias might be introduced into the knowledge induced. In this work, we analyze the use of the k-nearest neighbor as an imputation method. Imputation is a term that denotes a procedure that replaces the missing values in a data set with some plausible values. One advantage of this approach is that the missing data treatment is independent of the learning algorithm used. This allows the user to select the most suitable imputation method for each situation. Our analysis indicates that missing data imputation based on the k-nearest neighbor algorithm can outperform the internal methods used by C4.5 and CN2 to treat missing data, and can also outperform the mean or mode imputation method, which is a method broadly used to treat missing ...
903 citations
TL;DR: A large dataset compared to the state-of-the art is used and the proposed deep model performs dramatically shallow models, and they can be used as a practical tool for farmers to protect tomato against disease.
Abstract: Several studies have invested in machine learning classifiers to protect plants from diseases by processing leaf images. Most of the proposed classifiers are trained and evaluated with small datase...
762 citations
TL;DR: This book presents an overview of key concepts in argumentation theory and of formal models of argumentation in AI, beginning with a review of the foundational issues in argueation and formal argument modeling, and moving to more specialized topics, such as algorithmic issues, argumentations in multi-agent systems, and strategic aspects of argumentations.
Abstract: Argumentation in Artificial Intelligence examines the intersection between two fields of inquiry: Argumentation Theory and Artificial Intelligence. This book presents an overview of key concepts in argumentation theory and of formal models of argumentation in AI. Beginning with a review of the foundational issues in argumentation and formal argument modeling, the book then moves to more specialized topics, such as algorithmic issues, argumentation in multi-agent systems, and strategic aspects of argumentation. Finally, the volume addresses some practical applications of argumentation in AI and applications of AI in argumentation. Extensive examples are also provided to ensure that readers develop the right intuitions before they move from one topic to another. Knowledge of elementary logic is required, but the text contains an appendix furnishing such preliminaries.
579 citations
TL;DR: The probabilistic model presented is to be used by decision theoretic pedagogical agents to generate interventions aimed at achieving the best tradeoff between a user's learning and engagement during the interaction with educational games.
Abstract: We present a probabilistic model to monitor a user's emotions and engagement during the interaction with educational games. We illustrate how our probabilistic model assesses affect by integrating evidence on both possible causes of the user's emotional arousal (i.e., the state of the interaction) and its effects (i.e., bodily expressions that are known to be influenced by emotional reactions). The probabilistic model relies on a Dynamic Decision Network to leverage any indirect evidence on the user's emotional state, in order to estimate this state and any other related variable in the model. This is crucial in a modeling task in which the available evidence usually varies with the user and with each particular interaction. The probabilistic model we present is to be used by decision theoretic pedagogical agents to generate interventions aimed at achieving the best tradeoff between a user's learning and engagement during the interaction with educational games.
578 citations
TL;DR: The importance of data preparation in data analysis is shown, some research achievements in the area of data preparedness are introduced, and some future directions of research and development are suggested.
Abstract: Data preparation is a fundamental stage of data analysis. While a lot of low-quality information is available in various data sources and on the Web, many organizations or companies are interested in how to transform the data into cleaned forms which can be used for high-profit purposes. This goal generates an urgent need for data analysis aimed at cleaning the raw data. In this paper, we first show the importance of data preparation in data analysis, then introduce some research achievements in the area of data preparation. Finally, we suggest some future directions of research and development.
538 citations
Performance Metrics
| Year | Papers |
|---|---|
| 2026 | 1 |
| 2025 | 60 |
| 2024 | 143 |
| 2023 | 101 |
| 2022 | 141 |
| 2021 | 113 |