Supervised learning algorithms have shown limited use in the field of anomaly detection due
to the unpredictability and difficulty in acquiring abnormal samples. In recent years …

Background Diabetes, as a significant disease affecting public health, requires early
detection for effective management and intervention. However, imbalanced datasets pose a …

The last decade has seen an explosion in the collection of protein data. To actualize the
potential offered by this wealth of data, it is important to develop machine systems capable of …

Abstract Machine learning techniques such as logistic regression (LR), support vector
machine (SVM) and artificial neural network (ANN) were used to detect fasting blood …

Many domains would benefit from reliable and efficient systems for automatic protein
classification. An area of particular interest in recent studies on automatic protein …

In this work, we present an ensemble for automated music genre classification that fuses
acoustic and visual (both handcrafted and nonhandcrafted) features extracted from audio …

The possibility to detect activities of daily living (ADLs), with particular regards to fall
detection, is mandatory to implement a rigorous remote monitoring of frail people. Actually …

OBJECTIVES: Many classification problems must deal with data that contains missing
values. In such cases data imputation is critical. This paper evaluates the performance of …

The basic idea behind LBP is that an image is composed of micropatterns. A histogram of
these micropatterns contains information about the local features in an image. These …

An eye tracker's accuracy and system behavior play critical roles in determining the
reliability and usability of eye gaze data obtained from them. However, in contemporary eye …