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With the emerging technologies and all associated devices, it is predicted that massive amount of data will be created in the next few years – in fact, as much as 90% of current data were created in the last couple of years – a trend that will continue for the foreseeable future. Sustainable computing studies the process by which computer engineer/scientist designs computers and associated subsystems...
Nowadays, we see more and more cyber-attacks on major Internet sites and enterprise networks. Intrusion Detection System (IDS) is a critical component of such infrastructure defense mechanism. IDS monitors and analyzes networks' activities for potential intrusions and security attacks. Machine-learning (ML) models have been well accepted for signature-based IDSs due to their learn ability and flexibility...
In this study, we propose a new machine learning model namely, adaptive locality-effective kernel machine (Adaptive-LEKM) for protein phosphorylation site prediction. Adaptive-LEKM proves to be more accurate and exhibits a much stable predictive performance over the existing machine learning models. Adaptive-LEKM is trained using Position Specific Scoring Matrix (PSSM) to detect possible protein phosphorylation...
The accurate and stable prediction of protein domain boundaries is an important avenue for the prediction of protein structure, function, evolution, and design. Recent research on protein domain boundary prediction has been mainly based on widely known machine learning techniques. In this paper, we propose a new machine learning based domain predictor namely, DomNet that can show a more accurate and...
In this study, we propose a model that achieves both accurate modeling and sustainable model stability for corporate bankruptcy prediction. This model is to model the given samples accurately as well as to respond adequately to the unknown inputs by employing semiparametric approach where parametric model and nonparametric neural networks (NNs) are combined. By exploring the structural relationships...
The prediction of stock market has been an important issue in the field of finance, mathematics and engineering due to its great potential financial gain. In addition, uncertainty in the prediction of the financial time series has attracted interest from many researchers. In this study, we present recent developments in stock market prediction models, and discuss their strengths and limitations. In...
This paper surveys machine learning techniques for stock market prediction. The prediction of stock markets is regarded as a challenging task of financial time series prediction. In this paper, we present recent developments in stock market prediction models, and discuss their advantages and disadvantages. In addition, we investigate various global events and their issues on predicting stock markets...
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