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AIIO: Using Artificial Intelligence for Job-Level and Automatic I/O Performance Bottleneck Diagnosis

• Authors : Dong, Bin; Bez, Jean Luca; Byna, Suren

Subjects: Information and Computing Sciences; Artificial Intelligence; Bioengineering

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High-throughput phenotyping techniques for forage: Status, bottleneck, and challenges

• Authors : Tao Cheng; Dongyan Zhang; Gan Zhang

Subjects: Forage; High-throughput phenotyping; Precision identification

• Source: Artificial Intelligence in Agriculture, Vol 15, Iss 1, Pp 98-115 (2025)

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Patch-Level Feature Selection for Thoracic Disease Classification by Chest X-ray Images Using Information Bottleneck.

• Authors : Hung-Nguyen, Manh¹ (AUTHOR)

• Source: Bioengineering (Basel). Apr2024, Vol. 11 Issue 4, p316. 20p.

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ÜRETİM SİSTEMLERİNDE DARBOĞAZ TESPİTİ: LİTERATÜR ARAŞTIRMASI.

• Authors : AKKURT, Nagihan¹ ; HASGÜL, Servet¹

• Source: Uludag University Journal of the Faculty of Engineering (UUJFE). 2022, Vol. 27 Issue 3, p1285-1304. 20p.

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Artificial intelligence for throughput bottleneck analysis – State-of-the-art and future directions

• Authors : Subramaniyan, Mukund, 1989; Skoogh, Anders, 1980; Bokrantz, Jon, 1988

Subjects: Production system; Data-driven; Manufacturing system

• Source: DAIMP - Dataanalys inom underhållsplanering Journal of Manufacturing Systems. 60:734-751

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Congestion behavior and tolling strategies in a bottleneck model with exponential scheduling preference.

• Authors : Li, Chuanyao¹; Lu, Yichao¹; Wang, Yuqiang¹

• Source: Electronic Research Archive. 2023, Vol. 31 Issue 2, p1-24. 24p.

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Current Status, Prospect and Bottleneck of Ultrasound AI Development: A Systemic Review.

• Authors : Siyi Xun¹; Wei Ke¹; Mingfu Jiang¹

• Source: Advanced Ultrasound in Diagnosis & Therapy (AUDT). Jun2023, Vol. 7 Issue 2, p61-72. 12p.

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On The Effectiveness Of Bottleneck Information For Solving Job Shop Scheduling Problems Using Deep Reinforcement Learning

• Authors : Waubert de Puiseau, Constantin; Zey, Lennart; Demir, Merve

Subjects: Job Shop Scheduling Problems; Deep Reinforcement Learning; Artificial Intelligence

• Source: Proceedings of the Conference on Production Systems and Logistics: CPSL 2023 - 2 ; https://doi.org/10.15488/15326

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Deep bottleneck features for spoken language identification.

• Authors : Jiang B; National Engineering Laboratory of Speech and Language Information Processing, University of Science and Technology of China, Hefei, AnHui, China.; Song Y

Subjects: Artificial Intelligence* ; Language* ; Natural Language Processing*

• Source: PloS one [PLoS One] 2014 Jul 01; Vol. 9 (7), pp. e100795. Date of Electronic Publication: 2014 Jul 01 (Print Publication: 2014).Publisher: Public Library of Science Country of Publication: United States NLM ID: 101285081 Publication Model: eCollection Cited Medium: Internet

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Spiking neurons can learn to solve information bottleneck problems and extract independent components.

• Authors : Klampfl S; Institute for Theoretical Computer Science, Graz University of Technology, A-8010 Graz, Austria. ; Legenstein R

Subjects: Artificial Intelligence* ; Models, Neurological*; Action Potentials/Action Potentials/Action Potentials/*physiology

• Source: Neural computation [Neural Comput] 2009 Apr; Vol. 21 (4), pp. 911-59.Publisher: MIT Press Country of Publication: United States NLM ID: 9426182 Publication Model: Print Cited Medium: Print ISSN: 0899-7667 (Print)

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