Numerous deep learning architectures have been developed to accommodate the diversity
of time-series datasets across different domains. In this article, we survey common encoder …

Causal machine learning (ML) offers flexible, data-driven methods for predicting treatment
outcomes including efficacy and toxicity, thereby supporting the assessment and safety of …

Estimating counterfactual outcomes over time from observational data is relevant for many
applications (eg, personalized medicine). Yet, state-of-the-art methods build upon simple …

Abstract Machine learning (ML) holds great potential for accurately forecasting treatment
outcomes over time, which could ultimately enable the adoption of more individualized …

Although pairwise causal relations have been extensively studied in observational
longitudinal analyses across many disciplines, incorporating knowledge of causal pairs into …

Personalized clinical decision support systems are increasingly being adopted due to the
emergence of data-driven technologies, with this approach now gaining recognition in …

The concept of causality plays a significant role in human cognition. In the past few decades,
causal effect estimation has been well developed in many fields, such as computer science …

Many researchers aim towards solving human tasks with machine learning algorithms. In
this paper we equipped YOLOV-5 with fuzzy logic rules that can be used to solve reasoning …

Abstract Analysis of time series is a prominent issue in the field of data analysis. With large
amount of existing data in time series, multiple algorithms for analyzing time series data are …

Optimizing operational decisions, routine actions within some business or operational
process, is a key challenge across a variety of domains and application areas. The …