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2

SeizyML: An Application for Semi-Automated Seizure Detection Using Interpretable Machine Learning Models
Antonoudiou, Pantelis ; Basu, Trina ; Maguire, Jamie
Neuroinformatics. 23(2)

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3

SINr: a python package to train interpretable word and graph embeddings
Prouteau, Thibault ; Dugué, Nicolas ; Guillot, Simon ; et al.
French Regional Conference on Complex Systems. :215-215

Le Havre, France Graph embedding Word embedding Interpretability Frugal machine learning Plongements de graphes
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6

Helix 1.0: An Open-Source Framework for Reproducible and Interpretable Machine Learning on Tabular Scientific Data
Aguilar-Bejarano, Eduardo ; Lea, Daniel ; Sivakumar, Karthikeyan ; et al.

Machine Learning Artificial Intelligence
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7

dame-flame: A Python Library Providing Fast Interpretable Matching for Causal Inference
Gupta, Neha R. ; Orlandi, Vittorio ; Chang, Chia-Rui ; et al.

Computer Science - Machi... Computer Science - Mathe...
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8

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10

Modern approaches to building interpretable models of the property market using machine learning on the base of mass cadastral valuation
Tanashkina, Irina G. ; Tanashkin, Alexey S. ; Maksimchuik, Alexander S. ; et al.

Statistical Finance Machine Learning General Economics Economics Applications
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11

Interpretable Machine Learning with Python
Masis, Serg ; Masis, Serg ; Masis, Serg ; et al.

Electronic Resource
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12

Efficient hardware implementation of interpretable machine learning based on deep neural network representations for sensor data processing
Julian Schauer ; Payman Goodarzi ; Andreas Schütze ; et al.
Journal of Sensors and Sensor Systems. 14:169-185

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13

Interpretable radiomics-based machine learning model for differentiating glioblastoma from primary central nervous system lymphoma using contrast-enhanced T1-weighted imaging.
Xia, Xueming ; Tan, Qiaoyue ; Xie, Yuxin ; et al.
Scientific Reports. 11/4/2025, Vol. 15 Issue 1, p1-17. 17p.

RADIOMICS MACHINE learning FEATURE selection GLIOBLASTOMA multiforme CONTRAST-enhanced magnet... CENTRAL nervous system t...
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14

SurvLIMEpy: A Python package implementing SurvLIME
Pachón-García, Cristian ; Hernández-Pérez, Carlos ; Delicado, Pedro ; et al.
In Expert Systems With Applications 1 March 2024 237 Part C

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15

Interpretable machine learning model for forecasting compressive strength of aeolian sand concrete
Chen, Yun ; Fu, Qianwang ; Zheng, Wenbo
Frontiers of Structural and Civil Engineering. :1-19

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17

Explainable AI and Interpretable Machine Learning: A Case Study in Perspective
Vishwarupe, Varad ; Joshi, Prachi M. ; Mathias, Nicole ; et al.
In Procedia Computer Science 2022 204:869-876

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19

Aplicación de Machine Learning para predecir y explicar el rendimiento académico universitario.
Vladimir Vinces-Vinces, Fabricio ; Flores-Sanchéz, Miguel
Comunicar. Jul2025, Vol. 33 Issue 82, p12-23. 12p.

Machine learning School failure School dropouts Educational support Databases
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20

GraphAware: Interpretable machine learning on graphs
Daniel Walke ; Daniel Steinbach ; Alexander Schönhuth ; et al.

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