Zobrazeno 1 - 10
of 69
pro vyhledávání: '"Sindhgatta, Renuka"'
Autor:
Velmurugan, Mythreyi, Ouyang, Chun, Xu, Yue, Sindhgatta, Renuka, Wickramanayake, Bemali, Moreira, Catarina
Explainable Artificial Intelligence (XAI) techniques are used to provide transparency to complex, opaque predictive models. However, these techniques are often designed for image and text data, and it is unclear how fit-for-purpose they are when appl
Externí odkaz:
http://arxiv.org/abs/2410.12803
Autor:
Palaskar, Santosh, Ekambaram, Vijay, Jati, Arindam, Gantayat, Neelamadhav, Saha, Avirup, Nagar, Seema, Nguyen, Nam H., Dayama, Pankaj, Sindhgatta, Renuka, Mohapatra, Prateeti, Kumar, Harshit, Kalagnanam, Jayant, Hemachandra, Nandyala, Rangaraj, Narayan
The efficiency of business processes relies on business key performance indicators (Biz-KPIs), that can be negatively impacted by IT failures. Business and IT Observability (BizITObs) data fuses both Biz-KPIs and IT event channels together as multiva
Externí odkaz:
http://arxiv.org/abs/2310.20280
Recommending a sequence of activities for an ongoing case requires that the recommendations conform to the underlying business process and meet the performance goal of either completion time or process outcome. Existing work on next activity predicti
Externí odkaz:
http://arxiv.org/abs/2205.03219
Autor:
Wickramanayake, Bemali, He, Zhipeng, Ouyang, Chun, Moreira, Catarina, Xu, Yue, Sindhgatta, Renuka
In this paper, we address the "black-box" problem in predictive process analytics by building interpretable models that are capable to inform both what and why is a prediction. Predictive process analytics is a newly emerged discipline dedicated to p
Externí odkaz:
http://arxiv.org/abs/2109.01419
Modern data analytics underpinned by machine learning techniques has become a key enabler to the automation of data-led decision making. As an important branch of state-of-the-art data analytics, business process predictions are also faced with a cha
Externí odkaz:
http://arxiv.org/abs/2107.09767
Although modern machine learning and deep learning methods allow for complex and in-depth data analytics, the predictive models generated by these methods are often highly complex, and lack transparency. Explainable AI (XAI) methods are used to impro
Externí odkaz:
http://arxiv.org/abs/2106.08492
Predictive process analytics focuses on predicting the future states of running instances of a business process. While advanced machine learning techniques have been used to increase accuracy of predictions, the resulting predictive models lack trans
Externí odkaz:
http://arxiv.org/abs/2012.04218
Autor:
Moreira, Catarina, Chou, Yu-Liang, Velmurugan, Mythreyi, Ouyang, Chun, Sindhgatta, Renuka, Bruza, Peter
The use of sophisticated machine learning models for critical decision making is faced with a challenge that these models are often applied as a "black-box". This has led to an increased interest in interpretable machine learning, where post hoc inte
Externí odkaz:
http://arxiv.org/abs/2007.10668
This paper explores interpretability techniques for two of the most successful learning algorithms in medical decision-making literature: deep neural networks and random forests. We applied these algorithms in a real-world medical dataset containing
Externí odkaz:
http://arxiv.org/abs/2002.09192
Modern predictive analytics underpinned by machine learning techniques has become a key enabler to the automation of data-driven decision making. In the context of business process management, predictive analytics has been applied to making predictio
Externí odkaz:
http://arxiv.org/abs/1912.10558