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Network intrusion detection is the process of identifying malicious behaviors that target a network and its resources. Current systems implementing intrusion detection processes observe traffic at several data collecting points in the network but analysis is often centralized or partly centralized. These systems are not scalable and suffer from the single point of failure, i.e. attackers only need...
In this paper, a global algorithm for human action, facial and gesture recognition is presented. The proposed algorithm depends on the extraction of multiple transform domain features and Canonical Correlation Analysis (CCA) for features fusion and classification. The proposed algorithm achieved the best reported results for facial and facial expression recognition. Excellent comparable results were...
Deep learning has recently exhibited good performance in many applications. The convolution neural network is an often-used architecture for deep learning and has been widely used in computer vision and audio recognition, and outperformed other related handcraft designed feature in recent years. These techniques compared to other artificial intelligence algorithms and handcraft features need extremely...
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...
Support Vector Machine (SVM) is a popular machine learning technique for classification. SVM is computationally infeasible with large dataset due to its large training time. In this paper we compare three different methods for training time reduction of SVM. Different combination of Decision Tree (DT), Fisher Linear Discriminant (FLD), QR Decomposition (QRD) and Modified Fisher Linear Discriminant...
Extreme learning machine (ELM) is an efficient learning algorithm which can be easily used with least human intervene. But when ELM is applied as multiclass classifier, the results of some classes are not satisfactory and it's hard to adjust the parameters for these classes without affecting other classes. To overcome these limitations, a novel method is proposed. In proposed approach, binary ELM...
Anomaly detection systems rely on machine learning techniques to model the normal behavior of the system. This model is used during operation to detect anomalies due to attacks or design faults. Ensemble methods have been used to improve the overall detection accuracy by combining the outputs of several accurate and diverse models. Existing Boolean combination techniques either require an exponential...
The field of network and computer security is a never-ending race with attackers, trying to identify and patch software vulnerabilities before they can be exploited. In this ongoing conflict, it would be quite useful to be able to predict when and where the next software vulnerability would appear. The research presented in this paper is the first step towards a capability for forecasting vulnerability...
This paper describes, and illustrates using documented applications, a general framework methodology for wide-area forest and land use mapping and change detection using Synthetic Aperture Radar (SAR) remote sensing. Consideration is given to implementation of the SAR-based methodology using both commercial and free/open-source software. Our experience shows that constructing a complete processing...
Electromyografic signals offer insights into understanding the intent and extent of motion of the musculoskeletal system. This information could be utilized in developing controllers for applications such as prostheses and orthosis, and in general assistive technology. This paper presents a myoelectric based interface to control five discrete upper limb motions involving the shoulder and elbow joint...
Virtualized cloud systems are prone to performance anomalies due to various reasons such as resource contentions, software bugs, and hardware failures. It will be a daunting task for system administrators to manually keep track of the execution status of a large number of virtual machines all the time. Anomaly prediction is an effective approach to enhancing availability and reliability of Cloud infrastructures...
This paper presents a generalized pruning extreme learning machine (GP-ELM) algorithm which can generate a compact single-hidden-layer neural network (SLNN) by automatically pruning the number of hidden nodes iteratively while keep high accuracy. The proposed GP-ELM algorithm initializes a SLNN by using extreme learning algorithm (ELM) algorithm given superfluous number of hidden nodes. The following...
This paper proposes an efficient multimodal face recognition method by combining the textural as well as depth features, extracted from directional faces of input image. Directional faces are obtained using filters which are designed using Local Polynomial Approximation (LPA). The efficient modified Local Binary Pattern (mLBP) operator is used for the feature extraction from optimized directional...
During the last few years, imbalanced data classification issue has gained a great deal of attention. Many real life applications suffer from imbalanced distribution of data that can be handled by using different approaches such as data level, algorithm level or classifier ensembles. Single level as well as multi level classifier ensemble technique has shown improvement in classification performance...
Brain-machine interfaces (BMI) allow to decode motor commands from paralyzed patients' brains and use those commands with a rehabilitative or assistive purpose. However, brain non-stationarities can affect BMI performance over time in multi-session interventions. The amount and type of data used for calibration may play an important role on the posterior decoding performance. This paper studies six...
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)...
Expert systems for classification tasks in medical diagnosis systems require two properties. The true positives should be very high, as well as the true negatives, i.e. the system should correctly catch those who are ill, and correctly dismiss those who are healthy. The multi-modal evolutionary classifier uses a genetic algorithm to learn a reference vector for each class, and classification is done...
This paper propose a novel learning framework for classification of messages into spam and legit. We introduce a classification method based on feature space segmentation. Naive Bayes (NB) model is a statistical filtering process which uses previously gathered knowledge. Instead of using a single classifier, we propose the use of local and global classifier, based on Bayesian hierarchal framework...
Branch Retinal Vein Occlusion (BRVO) is one of the most common retinal diseases that could impair people's vision seriously if it is not timely diagnosed and treated. It would save a lot of time and money for both medical institutions and patients if BRVO could be well recognized automatically. In this paper, we propose to exploit Convolutional Neural Networks (CNN) for BRVO recognition. We propose...
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