Data-driven model with predictive ability are important to be used in medical and healthcare.
However, the most challenging task in predictive modeling is to construct a prediction model …

For incomplete data with mixed numerical and symbolic attributes, attribute reduction based
on neighborhood multi-granulation rough sets (NMRS) is an important method to improve …

Clustering techniques can group genes based on similarity in biological functions. However,
the drawback of using clustering techniques is the inability to identify an optimal number of …

Time consumption and energy consumption are essential indicators for evaluating the
effectiveness of task completion in multi-robot systems. On the basis of considering these …

Abstract In oncology, Deep Learning has shown great potential to personalise tasks such as
tumour type classification, based on per-patient omics data-sets. Being high dimensional …

With the progression of bioinformatics, applications of GE profiles on cancer diagnosis along
with classification have become an intriguing subject in the bioinformatics field. It holds …

This research focuses mainly on the binary class imbalance problem in data mining. It
investigates the use of combined approaches of data and algorithmic level solutions …

In oncology, Deep Learning has shown great potential to personalise tasks such as tumour
type classification, based on per-patient omics data-sets. Being high dimensional …

Early detection of disease genes helps humans to recover from certain gene-related
diseases, like genetic eye diseases. This work identifies the possibility of eye diseasesfor …

One of the challenging tasks in the arena of cancer classification is to identify the group of
genes that are correlated and are highly expressed in different types of tumor cells. Gene …