Split Decisions, Split Decisions

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Ladder Tagger—Splitting Decision Space to Boost Tagging Quality by Mariusz Paradowski, Adam Radziszewski

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Enhance triangular fuzzy parametric framework for solid multi objective transportation problem with split decision variables by Vishwas Deep Joshi, Medha Sharma, Lenka Čepová, Huda Alsaud, Kanak Kalita

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A probabilistic approach for eye-tracking based process tracing in catalog browsing by Erina Ishikawa Schaffer, Hiroaki Kawashima, Takashi Matsuyama

“…We evaluated our model using eye tracking data in digital catalog browsing and confirmed our model can split decision process into meaningful browsing states. …”
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From Research Project to Research Portfolio: Meeting Scale and Complexity by Jonathan Linton, Nicholas Vonortas

“…In general, high risk and a high frequency of split decisions tend to be replaced with lower risk initiatives. …”
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Fast sparse representative tree splitting via local density for large-scale clustering by Renmin Wang, Jie Li

“…This paper proposes a novel large-scale clustering framework with three key innovations: (1) Parameter-free cluster discovery: unlike conventional methods requiring predefined cluster numbers, our algorithm autonomously identifies natural cluster structures through dynamic density-based splitting decisions. (2) Hybrid sampling-partitioning strategy: by integrating randomized sampling with K-means-based partitioning, we extract high-quality representative points that preserve data integrity with linear computational complexity. (3) Local density-driven MST segmentation: A minimum spanning tree (MST) constructed from representatives is adaptively partitioned using a local density criterion, which dynamically disconnects weakly associated edges by comparing density peaks between adjacent representative points. …”
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Artificial intelligence-driven genotype–epigenotype–phenotype approaches to resolve challenges in syndrome diagnosticsResearch in context by Christopher C.Y. Mak, Hannah Klinkhammer, Sanaa Choufani, Nikola Reko, Angela K. Christman, Elise Pisan, Martin M.C. Chui, Mianne Lee, Fiona Leduc, Jennifer C. Dempsey, Pedro A. Sanchez-Lara, Hannah M. Bombei, John A. Bernat, Laurence Faivre, Frederic Tran Mau-Them, Irene Valenzuela Palafoll, Natalie Canham, Ajoy Sarkar, Yuri A. Zarate, Bert Callewaert, Ewelina Bukowska-Olech, Aleksander Jamsheer, Andreas Zankl, Marjolaine Willems, Laura Duncan, Bertrand Isidor, Benjamin Cogne, Odile Boute, Clémence Vanlerberghe, Alice Goldenberg, Elliot Stolerman, Karen J. Low, Vianney Gilard, Jeanne Amiel, Angela E. Lin, Christopher T. Gordon, Dan Doherty, Peter M. Krawitz, Rosanna Weksberg, Tzung-Chien Hsieh, Brian H.Y. Chung

“…Interpretation: We demonstrate the potential of using AI-based technologies to leverage genotype, phenotype and epigenetics data in facilitating splitting decisions in diagnosis of syndromes with minimal sample requirement. …”
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