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Titlebook: Artificial Intelligence Applications and Innovations; AIAI 2014 Workshops: Lazaros Iliadis,Ilias Maglogiannis,Christos Makris Conference pr

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樓主: legerdemain
41#
發(fā)表于 2025-3-28 15:10:09 | 只看該作者
42#
發(fā)表于 2025-3-28 19:22:39 | 只看該作者
Explainable AI for?Mixed Data Clusteringbed preliminary experiments were performed in order to tune up settings and procedures, analysing the encountered problems and their respective solutions. A methodology consisting of five large-scale experiments is proposed in order to properly validate and improve the evaluation techniques used in OpenFlow scenarios.
43#
發(fā)表于 2025-3-29 02:39:06 | 只看該作者
44#
發(fā)表于 2025-3-29 04:02:58 | 只看該作者
45#
發(fā)表于 2025-3-29 10:09:26 | 只看該作者
Comparative Evaluation of Feature Extraction Methods for Human Motion Detectionotion signals. At each step, state-of-the-art methods are applied, and the produced results are finally compared in order to evaluate the importance of the applied feature extraction and preprocessing combinations, for the human activity recognition task.
46#
發(fā)表于 2025-3-29 15:19:36 | 只看該作者
Conference proceedings 2014al Intelligence Applications and Innovations, AIAI 2014, held in Rhodes, Greece, in September 2014: the Third Workshop on Intelligent Innovative Ways for Video-to-Video Communications in Modern Smart Cities, IIVC 2014; the Third Workshop on Mining Humanistic Data, MHDW 2014; the Third Workshop on Co
47#
發(fā)表于 2025-3-29 19:20:06 | 只看該作者
48#
發(fā)表于 2025-3-29 20:32:42 | 只看該作者
Explainable AI for?Mixed Data Clusteringome, and capable of keeping the medical history and digital records of every patient in the Glaucoma Department. In addition, a specific web camera with snapshot ability of high quality photo of the eye has utilised. Two patients have been initially enrolled in the study and the preliminary results are so presented.
49#
發(fā)表于 2025-3-30 01:51:05 | 只看該作者
50#
發(fā)表于 2025-3-30 04:22:44 | 只看該作者
AutoCL: AutoML for?Concept Learning R we can capture user behavior more accurately. We present a new evaluation metric called Reciprocal Rank using Webpage Popularity (RRP) which takes into account not only the document’s relevance judgment, but also its popularity, and as a result correlates better with click metrics than the other evaluation metrics do.
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