MOTIVATION Prediction of protein folding patterns is one level deeper than that of protein structural classes, and hence is much more complicated and difficult. To deal with such a challenging problem, the ensemble classifier was introduced. It was formed by a set of basic classifiers, with each trained in different parameter systems, such as predicted secondary structure, hydrophobicity, van d...

We propose a Bayesian framework for recursively estimating the classifier weights in online learning of a classifier ensemble. In contrast with past methods, such as stochastic gradient descent or online boosting, our framework estimates the weights in terms of evolving posterior distributions. For a specified class of loss functions, we show that it is possible to formulate a suitably defined ...

In recent times, the manufacturing processes are faced with many external or internal (the increase of customized product re-scheduling, process reliability,.. ) changes. Therefore, monitoring and quality management activities for these manufacturing processes are difficult. Thus, the managers need more proactive approaches to deal with this variability. In this study, a proactive quality monit...

The objective of this study is to develop an ensemble classifier with Merit Merge feature selection that will enhance efficiency of classification in a multivariate multiclass medical data for effective disease diagnostics. The large volumes of features extracted from brain Magnetic Resonance Images and neuropsychological tests for diagnosis lead to more complexity in classification procedures....

Gesture recognition has been studied for a while within the fields of computer vision and pattern recognition. A gesture can be defined as meaningful physical movement fingers, hands, arms, or other parts body with purpose to convey information environment interaction. For instance, hand (HGR) used recognize sign language which is primary means communication by deaf mute. Vision-based HGR criti...

Generally, the risks associated with malicious threats are increasing for Internet of Things (IoT) and its related applications due to dependency on minimal resource availability IoT devices. Thus, anomaly-based intrusion detection models networks vital. Distinct methodologies need be developed Industrial (IIoT) network as threat is a significant expectation stakeholders. Machine learning appro...

For most real-world data streams, the concept about which is obtained may shift from time to time, a phenomenon known as drift. applications such nonstationary time-series data, drift often occurs in cyclic fashion, and previously seen concepts will reappear, supports unique kind of recurring concepts. A cyclically drifting exhibits tendency return visited states. Existing machine learning algo...

Recently, ensemble learning algorithms are proposed to address the challenges of high dimensional classification for steganalysis caused by the curse of dimensionality and obtain superior performance. In this paper, we extend the state-of-the-art steganalysis tool developed by Kodovsky and Fridrich: the Kodovsky’s ensemble classifier and propose a novel method, called CSRS for high-dimensional ...