Digital twins have emerged as a key technology for optimizing the performance of engineering products and systems. High-fidelity numerical simulations constitute backbone design, providing an accurate insight into complex However, large-scale, dynamic, non-linear models require significant computational resources are prohibitive real-time digital twin applications. To this end, reduced order (R...

Machine learning is practical for software engineering problems, even in datastarved domains. When data is scarce, knowledge can be farmed from seeds; i.e. minimal and partial descriptions of a domain. These seeds can be grown into large datasets via Monte Carlo simulations. The datasets can then be harvested using machine learning techniques. Examples of this knowledge farming approach, and th...

Modern studies in the field of environment science and engineering show that deterministic models struggle to capture the relationship between the concentration of atmospheric pollutants and their emission sources. The recent advances in statistical modeling based on machine learning approaches have emerged as solution to tackle these issues. It is a fact that, input variable type largely affec...

Artificial intelligence (AI) and machine learning (ML) has been a major research interest in computer science for the better part of the last few decades. However, all too recently, both AI and ML have rapidly grown to be media frenzies, pressuring companies and researchers to claim they use these technologies. As ML continues to percolate into the layman's life, we, as computer scientists and ...

In this position paper, I first describe a new perspective on machine learning (ML) by four basic problems (or levels), namely, “What to learn?”, “How to learn?”, “What to evaluate?”, and “What to adjust?”. The paper stresses more on the first level of “What to learn?”, or “Learning Target Selection”. Towards this primary problem within the four levels, I briefly review the existing studies abo...

The use of machine learning (ML) has been increasing rapidly in the medical imaging field, including computer-aided diagnosis (CAD), radiomics, and medical image analysis. Recently, an ML area called deep learning emerged in the computer vision field and became very popular in many fields. It started from an event in late 2012, when a deep-learning approach based on a convolutional neural netwo...

Machine Learning (ML) is a research subarea of Artificial Intelligence that aims to develop computer programs that can evolve with new experiences. Among the many ML goals, the endless learning, i.e., methods that would enable computer systems to autonomously improve their own performance, based on previously learnt information, is of particular interest in the research described in this paper....

background: today, despite the many advances in early detection of diseases, cancer patients have a poor prognosis and the survival rates in them are low. recently, microarray technologies have been used for gathering thousands data about the gene expression level of cancer cells. these types of data are the main indicators in survival prediction of cancer. this study highlights the improvement...

My research interests lie in Machine Learning (ML) and its applications to Information Retrieval (IR), Text analysis and Data Mining (DM). These applied areas have historically been driven by empirical approaches. While these approaches have been quite successful in terms of performance, one of their major drawbacks is their lack of easy interpretability. It is my firm belief that machine learn...

Researchers have embraced a variety of machine learning (ML) techniques in their efforts to improve the quality of learning programs. The recent evolution of hybrid architectures for machine learning systems has resulted in several approaches that combine rule-induction methods with case-based reasoning techniques to engender performance improvements over moretraditional one-representation arch...