In this paper, we propose a two-stage multi-task learning representation method for the classification of synthetic aperture radar (SAR) target images. The first stage of the proposed approach uses multi-features joint sparse representation learning, modeled as a ℓ 2 , 1 -norm regularized multi-task sparse learning problem, to find an effective subset of training samples. Then, a new dictionary...

During the past decade, the number of mobile electronic devices equipped with cameras has increased dramatically and so has the number of real-world applications for image classification. In many of these applications, the image data is captured in an uncontrolled manner and in complex environments and conditions under which existing image classification techniques may not perform well. In this...

this paper presents a new strategy based on multi-stage fuzzy (msf) pid controller based on artificial bee colony (abc) for damping power system stabilizer (pss) in multi-machine environment. the recent studies in artificial intelligence demonstrated that the abc optimization is strong intelligent method in complicated stability problems. also, finding the parameters of pid controller in power ...

Sleep stage classification plays a significant role in the accurate diagnosis and treatment of sleep-related diseases. This study aims to develop an efficient deep learning based scheme for correctly identifying sleep stages using multi-biological signals such as electroencephalography (EEG), electrocardiogram (ECG), electromyogram (EMG), electrooculogram (EOG). Most prior studies focus on hand...

Sleep plays a vital role in optimum working of the brain and body. Numerous people suffer from sleep-oriented illnesses like apnea, insomnia, etc. stage classification is primary process quantitative examination polysomnographic recording. scoring mainly based on experts’ knowledge which laborious time consuming. Hence, it can be essential to design automated sleep model using machine learning ...

In this paper, it is proposed a new methodology based on invariant moments and multi-class support vector machine (MCSVM) for classification of human parasite eggs in microscopic images. The MCSVM is one of the most used classifiers but it has not used for classification of human parasite eggs to date. This method composes four stages. These are pre-processing stage, feature extraction stage, c...

A serial multi-stage classification system for facing the problem of intrusion detection in computer networks is proposed. The whole decision process is organized into successive stages, each one using a set of features tailored for recognizing a specific attack category. All the stages employ suitable criteria for estimating the reliability of the performed classification, so that, in case of ...