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An efficient hybrid prediction approach for predicting cloud consumer resource needs
(
IEEE Computer Society
, 2016 , Conference Paper)
The prediction of cloud consumer resource needs is a vital step for several cloud deployment applications such as capacity planning, workload management, and dynamic allocation of cloud resources. In this paper, we develop ...
A support vector machine-based method for LPV-ARX identification with noisy scheduling parameters
(
Institute of Electrical and Electronics Engineers Inc.
, 2014 , Conference Paper)
In this paper, we present a method that utilizes support vector machines (SVM) to identify linear parameter-varying (LPV) auto-regressive exogenous input (ARX) models corrupted by not only noise, but also uncertainties in ...
A kernel-based approach to MIMO LPV state-space identification and application to a nonlinear process system
(
Elsevier B.V.
, 2015 , Conference Paper)
This paper first describes the development of a nonparametric identification method for linear parameter-varying (LPV) state-space models and then applies it to a nonlinear process system. The proposed method uses kernel-based ...
An IV-SVM-based approach for identification of state-space LPV models under generic noise conditions
(
Institute of Electrical and Electronics Engineers Inc.
, 2015 , Conference Paper)
This paper presents a nonparametric identification method for state-space linear parameter-varying (LPV) models using a modified support vector machine (SVM) approach. While most LPV identification schemes in the state-space ...
Evidence theory-based approach for epileptic seizure detection using EEG signals
(
IEEE
, 2012 , Conference Paper)
Electroencephalogram (EEG) is one of the potential physiological signals used for detecting epileptic seizure. Discriminant features, representing different brain conditions, are often extracted for diagnosis purposes. ...
EEG feature extraction and selection techniques for epileptic detection: A comparative study
(
IEEE Computer Society
, 2013 , Conference Paper)
Epileptic detection techniques rely heavily on the Electroencephalography (EEG) as representative signal carrying valuable information pertaining to the current brain state. For these techniques to be efficient and reliable, ...
Learned vs. hand-designed features for ECG beat classification: A comprehensive study
(
Springer Verlag
, 2017 , Conference Paper)
In this study, in order to find out the best ECG classification performance we realized comparative evaluations among the state-of-the-art classifiers such as Convolutional Neural Networks (CNNs), multi-layer perceptrons ...
Performance Comparison of Learned vs. Engineered Features for Polarimetric SAR Terrain Classification
(
Institute of Electrical and Electronics Engineers Inc.
, 2019 , Conference Paper)
In this work, we propose to use learned features for terrain classification of Polarimetric Synthetic Aperture Radar (PolSAR) images. In the proposed classification framework, the learned features are extracted from sliding ...
Arabic handwriting recognition using sequential minimal optimization
(
Institute of Electrical and Electronics Engineers Inc.
, 2017 , Conference Paper)
Due to the variability of writing styles and to other problems related to the nature of Arabic scripts, the recognition of Arabic handwriting is still awaiting accurate results. Segmentation of Arabic handwritten words ...
Letter-based classification of Arabic scripts style in ancient Arabic manuscripts
(
Institute of Electrical and Electronics Engineers Inc.
, 2017 , Conference Paper)
Classifying ancient Arabic manuscripts based on handwriting styles is one of the important roles in the field of paleography. Recognizing the style of handwriting in Arabic manuscripts helps in identifying the origin and ...