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Network forensics is addressed to deal with cybercrime. The main purpose of a network forensics system is reconstructing evidences of network attacks. In order to reconstruct evidence, the network attack is firstly identified. Therefore, network attack detection solutions play an important role in network forensics. There are two main types of network attacks: network level and application level....
Automation of Electroencephalogram (EEG) analysis can significantly help the neurologist during the diagnosis of epilepsy. During last few years lot of work has been done in the field of computer assisted analysis to detect an epileptic activity in an EEG. Still there is a significant amount of need to make these computer assisted EEG analysis systems more convenient and informative for a neurologist...
Network traffic classification gains continuous interesting while many applications emerge on the different kinds of networks with obfuscation techniques. Decision tree is a supervised machine learning method used widely to identify and classify network traffic. In this paper, we introduce a comparative study focusing on two common decision tree methods namely: C4.5 and Random forest. The study offers...
In biomedical area, information is mainly in natural language text format. Such information is stored in huge repositories. It is not easy to access required information from this large amount of data. Also the classification systems developed for general text is not applicable for biomedical data. The biomedical researchers need fast and accurate information accessing tools for extracting useful...
Contemporary manufacturing scheduling has still limitations in real-world environments where disturbances on working conditions could occur over time. Therefore, human intervention is required to maintain real-time adaptation and optimization and efficiently adapt to the inherent dynamic of markets. This paper addresses the problem of incorporating rush orders into the current schedule of a manufacturing...
Elasticity is one of the key benefits of cloud computing which helps customers reduce the cost. Although elasticity is beneficiary in terms of cost, obligation of maintaining Service Level Agreements leads to necessity in dealing with the cost-performance trade-off. Proactive auto-scaling is an efficient approach to overcome this problem. In this approach scaling actions are generated based on prediction...
Identifying ocean wave conditions is very important to marine related activities since it reflects the severity of current waves. One main issue is that for accurate identification of wave conditions, longer time series data are needed. However, these pose major impediment to real-time publishing of wave conditions. This study explores the possibility of classifying wave conditions given only shorter...
One of the main problems in image based plant identification has been the lack of quality training image data. A few attempts for solving this problem through generating high quality plant images from crowd sourced Web image collections like Flickr are proposed in this paper. These methods try to automatically identify correct and informative training images from those Web images, which typically...
In the present world, the web is the firmest and most common medium of communication and business interchange. Every day, millions of data are loaded through various channels on the web by users and user input can be malicious. Therefore, security becomes a very important aspect of web applications. Since they are easily accessible, they are prone to many vulnerabilities which if neglected can cause...
Reliable detection and recognition of facial expression from still images in the unconstrained real world situations has many potential applications. Smile detection can be used in many applications include modeling systems for psychological studies on human emotional responses, expression recognition technologies, extending image search capabilities etc. This paper proposes an experimental study...
There are several diseases which arise because of changes in the microbial communities in the body. Scientists continue to conduct research in a quest to find the catalysts that provoke these changes in the naturally occurring microbiota. Bacterial Vaginosis (BY) is a disease that fits the above criteria. BV afflicts approximately 29% of women in child bearing age. Unfortunately, its causes are unknown...
When we apply AdaBoost in pedestrian detection, a large number of examples are needed to train a detector. Except for designing features, a reasonable utilization of training examples is also significant to the detection accuracy and training time. In this paper, we propose a new method, named Weight-Loss Control Sampling (WLCS), to deal with the negative training examples by improving the training...
This paper proposes a method to learn the variation of solar panel Maximum Power Point (MPP) parameters as functions of environmental conditions using Gaussian Process (GP) based machine learning. As a result of using GP, functions are learned along with the additional information of their uncertainty margins. The paper discusses about learning three functions specifically, where each of them take...
Indoor positioning is a basic requirement of Intelligent Environments. It is a building block for providing context-aware computing. Infrastructure based WiFi fingerprinting positioning technology (IBWFP) is one way of satisfying such requirement. In IBWFP, the basic landmarks for positioning are fixed 802.11 access points with a well known location. And it is infrastructure based because it refers...
Breast cancer is the world's second most frequent type of cancer and in Japan it is the third most frequent one. The prognosis of its recurrence, after a first treatment, is very important to increase the survival rate of a patient. This work shows the application of the k-Nearest Neighbors (kNN) method to prognosis breast cancer and also proposes a method to select a good setting with the parameters...
The bag of words (BOW) representation of documents is very common in text classification systems. However, the BOW approach ignores the position of the words in the document and more importantly, the semantic relations between the words. In this study, we present a simple semantic kernel for Support Vector Machines (SVM) algorithm. This kernel uses higher-order relations between terms in order to...
Email classification is an important topic in literature attempting to correctly classify user emails and filter out spam emails. In this paper, we identify some challenges regarding this topic and propose an effective email classification model based on both data reduction and disagreement-based semi-supervised learning. In particular, the main objective of the data reduction is to select an optimum...
Classification of malicious code by machine learning gives more flexible and adaptable prediction result than by existing approaches [1]. But the approach just can identify looks-like malicious code instead of real malicious one. In this research, a novel method to reduce the vagueness in the classification by machine learning to consider code sequence.
This paper proposes a method to simultaneously select the most relevant single nucleotide polymorphisms (SNPs) markers — the attributes — for the characterization of any measurable phenotype described by a continuous variable using support vector regression (SVR) with Pearson VII Universal Kernel (PUK). The proposed study is multiattribute towards considering several markers simultaneously to explain...
This paper presents an empirical study on selecting a small amount of useful unlabeled data with which the classification accuracy of semi-supervised learning algorithms can be improved. In particular, a hybrid method of unifying the simply recycled selection method and the incrementally reinforced selection method is considered and empirically evaluated. The experimental results, obtained using well-known...
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