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MIX-AI 2018: MIX Summit and Conference on Artificial Intelligence and Machine Learning

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MIX-AI
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投稿締切日:
2018-03-16
通知日:
2018-04-20
開催日:
2018-07-24
開催地:
Dublin, Ireland
閲覧: 13437   フォロー: 0   参加: 0

会伴インデックス (CP-I)

42.1 / 100
全 5,652 件中 第 4,093 位 · 上位 73%

人工知能・機械学習 分野 736 件中 第 527 位

根拠が限られています：この会議は CCF / ICORE / QUALIS のいずれにも収録されておらず、採択率のデータもないため、スコアの大部分は中立の基準値に戻ります。
学術的評価 (35%) データなし — 中立の基準値 50 点として算入 —
投稿の選択性 (20%) データなし — 中立の基準値 50 点として算入 —
開催回数 (20%) データなし — 中立の基準値 50 点として算入 —
コミュニティの注目度 (10%)
8
公開情報の充実度 (15%)
25

使用した入力: 過去 24 か月にこのページを開いた研究者：2 人

公開情報で不足しているもの: 過去の採択率 (+4.5) · 過去の開催回 (+3.0) · 最優秀論文の記録 (+2.3)
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信頼度 25% — スコアのうち、中立の基準値ではなく実際に観測されたデータに基づく割合。 このスコアの算出方法 · ランキングを見る · アルゴリズム版 1.1 · 算出日 2026-09-08

論文募集

MIX-AI 2018 (MIX Summit and Conference on Artificial Intelligence and Machine Learning) is an academic conference held in Dublin, Ireland on 2018-07-24. The paper submission deadline is 2018-03-16. Acceptance notifications are sent on 2018-04-20.

MIX-AI will present some of the latest high-quality results and breakthroughs in Artificial Intelligence. The topics are framed to match the European Commission's Horizon 2020 societal challenges. It is planned that the results and papers presented at the conference will constitute a significant and holistic contribution to the growing knowledge based within the area of AI. To ensure this, the conference will consider and present research that is related to addressing the following areas: 1. AI for Health and Wellbeing 2. AI for Food/Agriculture & the Bioeconomy 3. AI for Energy & Environment 4. AI for Transportation 5. AI and its impact on Society 6. AI for Business & Finance If your research is related to any of these topics, then we welcome a submission from you. Submissions can be from anywhere in the world and from any discipline. MIX-AI uses EasyChair software for paper submissions. If you do not have an EasyChair account, then you will need to register one in order to submit. You can register and/or submit your paper by clicking the 'Submit Paper' button below. Submission Information ​ Accepted Submissions Types Full papers Papers in this category concern the traditional full papers of 12 pages. These should be aimed at presenting new and original work in Artificial Intelligence that meets the high-quality standards of MIX-AI. Mandatory abstract submissions of at most 200 words for the full papers are due by the deadline of 16th of March 2018; submissions of the final version of the entire full paper are due by the deadline of 11th of May 2018. Poster-only papers/Short Papers Submissions are allowed that are targeted at being a poster-only paper of at most 2 pages, provided that if they are chosen for submission they are then extended to a short paper of at least 4 pages. Such papers should present work in Artificial Intelligence that is original, but has not yet reached the stage of the more mature, complete research results that are typically published in full papers at MIX-AI. Poster-only papers are due by the deadline of 16th of March 2018. Both full and short papers must be done in accordance to the publisher's guidelines. Word templates that fit such guidelines can be downloaded here. Review Process Each paper submitted to MIX-AI will be rigorously evaluated in a double-blind peer review process. Evaluation is done on a per-track basis, ensuring high interest and high expertise of the reviewers. Review criteria include the significance of the work, technical soundness, novelty, clarity, writing quality, relevance, and sufficiency of information to permit replication, if applicable. Accepted papers will be given feedback on a TBC date, allowing authors to update their papers before full submission. Submission Terms Submit a final, revised, version to MIX-AI on or before the final deadline. Register at least one author to attend the conference on or before the registration deadline Attend the conference (at least one author) Present the accepted paper at the conference Anonymity Submitted papers should be anonymised. This means that they should NOT contain any element that may reveal the identity of their authors. This includes author names, affiliations, and acknowledgments. Moreover, any references to any of the author's own work should be made as if the work belonged to someone else.
最終更新：Dou Sun（）

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