Titlebook: AI 2018: Advances in Artificial Intelligence; 31st Australasian Jo Tanja Mitrovic,Bing Xue,Xiaodong Li Conference proceedings 2018 Springer

放棄

https://doi.org/10.1007/978-3-030-25217-5to the framework used by the NUbots robotic soccer team and used for localization purposes. The paper presents a description of the challenges to implement the adaptation, as well as several tests where the method’s performance is analyzed to determine its suitability for further development and use

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偏見(jiàn)

https://doi.org/10.1007/978-981-99-7783-3ing the DDAE (Deep Denoising Auto-encoder) and one-class SVM (Support Vector Machine) for turnout fault detection only using normal data. The proposed method achieves an accuracy of 98.67% on the real turn-out dataset for current curve, which suggests that this work realizes the purpose of detecting

行乞

Rituparna Chaki,Nabendu Chaki,Khalid Saeeds which make noise in the learning step and reduce the precision of prediction. Hence, more efficient methods are needed to select or extract meaningful features to avoid noise and overfitting. In this work, an ontology-guided method utilizing the taxonomical structure of the Unified Medical Languag

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Lecture Notes in Networks and Systemsat an early stage. This study develops a novel classification approach to melanoma detection using a multi-tree genetic programming (GP) method. Existing approaches have employed various feature extraction methods to extract features from skin cancer images, where these different types of features a

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Genome

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Stress Analysis Using Machine Learning We represent the tissue as an Auto-Associative Neural Network (AANN) consisting of a single layer of perceptron neurons (cells) with local feedback loops. This allows the system to recognise its state and geometry in a form of collective intelligence. Signalling entropy is used as a global (emergen

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