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Need of effective and efficient Intrusion Detection System, used the concept of hybrid approach in Iintrusion Detection System where many combination of different techniques has been used so far. In this paper, proposed hybrid approach which is the combination of Fuzzy C-Mean (FCM), a clustering technique and Support Vector Machines (SVM) will be compared with K-Means and SVM, K-Means and Naïve bayes,...
This paper describes an algorithm that parallelizes support vector machines. The data is split into subsets and optimized separately with multiple SVMs, instead of analyzing the whole training set in one optimization step. The partial results are combined and filtered in a cascade of SVMs. The process terminates when the global optimum is reached. The Cascade SVM is spread over multiple processors...
Most of the intrusion detection systems analyze all network traffic features to identify intrusions with different classification techniques. Any intrusion detection model developed has to provide maximum accuracy with minimal false alarms. Identifying the optimal feature subset for classification is an important task for improved classification. In this paper, consistency based feature selection...
Blogs, review sites, Twitter, and other social networks are the most common platforms that are used by people and organizations for posting their views. People's opinions, emotions, and attitude towards particular aspect can studied through opinion mining. It has been observed through this study that higher accuracy has been achieved by constructing an ensemble using bagging with single classifiers...
Band selection is an effective solutions for dimensionality reduction in hyperspectral imagery. In this paper, a novel band weighting and selection method is proposed based on maximizing margin in support vector machine (SVM). The goal is to reduce high dimensionality if hyperspectral data while achieving accuracy classification performance. This method computes the weights of the samples to maximize...
Rough set theory is a paradigm to deal with uncertainty, vagueness, and incompleteness of data. Although it has been applied successfully to feature selection in different application domains, it is seldom used for the analysis of hyperspectral images. In this paper, a rough set based supervised method is proposed to select informative bands in hyperspectral images. The proposed technique exploits...
We present a new approach for remote sensing image classification. The methodology combines many related tasks namely non linear source separation, feature extraction, feature fusion and learning classification. Nonlinear source separation is a pre-processing stage that aims to compensate the nonlinear mixing natural phenomenon. Latent signals, called sources are transformed to the feature presentation...
Plants are fundamental for human beings, so it's very important to catalog and preserve all the plants species. Identifying an unknown plant species is not a simple task. Automatic image processing techniques based on leaves recognition can help to find the best features useful for plant representation and classification. Many methods present in literature use only a small and complex set of features,...
Domain adaptation methods show better ability to learn when the training data is not identically and independently distributed. The key task of domain adaptation is to find a suitable measure to scale the distributed difference between source domain and target domain. So a projected maximum divergence discrepancy distance measure is proposed. Based on the structural risk minimization theory and the...
Whilst distributed generation, particularly in the form of solar photovoltaics, is appearing as one of the key components of future power systems, it brings many challenges. Challenges such as intermittent generation supply, fault detection, anti-islanding operation and voltage control have received a great deal of research and practical attention. One practical challenge that hasn't been examined...
A method for sentiment polarity assignment for textual content written in Polish using supervised machine learning approach with transfer learning scheme is proposed in the paper. It has been shown that performing simple natural language processing steps prior to classification, provides inspiring results without redundant computation overhead. The documents containing subjective opinions were classified...
The unprecedented growth of data in web, social media and the attempt to make the cognitive process using computers make Sentiment Analysis a challenging and interesting research problem. Sentiment Analysis mainly deals with the process of analyzing the sentiments or feelings from someone's expression or piece of information, and also in discovering the cognitive behavior of humans. The usage of computers...
Author attribution has grown into an area that is more challenging from the past decade. It has become an inevitable task in many sectors like forensic analysis, law, journalism and many more as it helps to detect the author in every documentation. Here unigram/bigram features along with latent semantic features from word space were taken and the similarity of a particular document was tested using...
When binary tree SVM is used for multi-class fault diagnosis, inner-class distance or between-class distance is always used to decide the classification hierarchy, but these methods cannot take the comprehensive separability information between classes into account, which leads to decrease the accuracy of fault diagnosis easily, so an improved binary tree SVM method is proposed. Combining the separability...
This paper introduces a novel approach to predict human motion for the Non-binding Lower Extremity Exoskeleton (NBLEX). Most of the exoskeletons must be attached to the pilot, which exists potential security problems. In order to solve these problems, the NBLEX is studied and designed to free pilots from the exoskeletons. Rather than applying Electromyography (EMG) and Ground Reaction Force (GFR)...
In digital retinal images, positive means of texture feature detection around the macula region with specified radius is still an open issue. Diabetic macular edema is a complication caused due to Diabetic Retinopathy (DR) and is the true cause of blindness and visual loss. In this paper, we have presented a computerized method for texture feature extraction around the specified radius taking macula...
Kernel methods for classification is a well-studied area in which data are implicitly mapped from a lower-dimensional space to a higher-dimensional space to improve classification accuracy. However, for most kernel methods, one must still choose a kernel to use for the problem. Since there is, in general, no way of knowing which kernel is the best, multiple kernel learning (MKL) is a technique used...
Cost-performance trade off is one of the critical challenges in cloud computing environments. Predictive auto-scaling systems mitigate this issue by scaling in/out system automatically based on performance prediction results. The goal of this research is to investigate the impact of different prediction results on the scaling actions generated by predictive auto-scaling systems. In this study, predictive...
Driver drowsiness may cause traffic injuries and death. In literature, various methods, for instance, image-based, vehicle-based, and biometric-signals-based, have been proposed for driver drowsiness detection. In this paper, a new approach using Electrocardiogram is discussed. Performance evaluation is carried out for the driver drowsiness classifier. The developed classifier yields overall accuracy,...
Similarity learning ranges over an extensive field in machine learning and pattern recognition. This paper deals with similarity learning based on multiple support vector data description (SVDD). It is well known that SVDD was proposed for one-class or two-class unbalanced learning problems. Thus, we propose a multiple SVDD (MSVDD) algorithm and apply it to multi-class learning problems. A SVDD model...
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