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As the attackers nowadays are getting craftier it is deemed important to have a security system which is easy to maintain and economically affordable and gives suitable defense against attacks both known and novel. In this paper, the concept of genetic programming is applied to recreate open network conditions, using records obtained from KDD Cup'99 dataset. Then the newly created records (network...
In this paper, we propose a hybrid method for intrusion detection which is based on k-means, naive-bayes and back propagation neural network (KBB). Initially we apply k-means which is partition-based, unsupervised cluster analysis method. In the form of clusters, we attain the gathered data which can be easily processed and learned by any machine learning algorithm. These outcomes are provided to...
This paper proposes a novel method for discriminating the supraventricular tachycardias and the ventricular tachycardias via a high dimensional linear discriminant function and a perceptron with a multi-piece domain activation function having multi-level functional values. The algorithm is implemented via the mobile application. First, the discrete cosine transform is applied to each training electrocardiogram...
The bag of visual words is a well established representation in diverse computer vision problems. Taking inspiration from the fields of text mining and retrieval, this representation has proved to be very effective in a large number of domains. In most cases, a standard term-frequency weighting scheme is considered for representing images and videos in computer vision. This is somewhat surprising,...
In this work, for a given a set of code vector assignments to an input by a multistage residual vector quantizer RVQ [1], Bayesian framework is formulated to find the most probable class membership of the input. Furthermore, Markov structure is also used to improve the memory cost of the classification.
The awareness of choosing the right option for career is increasing among the students. The students fail to know their strengths and choose career randomly which leads to frustration and demoralization. The automated systems used by the counsellors are to evaluate the personality traits of the individual. The accuracy of prediction depends on the set of relevant skill parameters and analytics on...
Classification is the category that consists of identification of class labels of records that are typically described by set of features in dataset. The paper describes a system that uses a set of data pre-processing activities which includes Feature Selection and Discretization. Feature selection and dimension reduction are common data mining approaches in large datasets. Here the high data dimensionality...
Intrinsically disorder regions (IDRs) or, proteins (IDPs) are associated with important biological functions, while lacking stable structure in their native state. The phenomena of disordered proteins or residues are abundant in nature and are extensively involved in critical human diseases and hence impacting drug discovery. Thus, the study using disorder prediction is becoming crucial in the proteomic...
Big data is a set of very large and complex data that is hard to load on computers. The main challenge in big data world is related to their search, categorize and analyze specially, when they are unbalanced. Despite, there are a lot of works in the field of big data but analyzing unbalanced big data is still a fundamental challenge in this area. In this paper we try to solve the problem of RSIO-LFCM...
An electric wheelchair is basically acknowledged for mobility improvement in disability patients. In some cases, their hand could not well function. They may tire easy before reaching to the desired destination. Furthermore, the safety is the most concerned issue for wheelchair control in disability patients. Therefore, this work tries to develop the prototype of the automated navigation system that...
Text categorization refers to the task of designing methods to automatically classify text documents into different groups. With wide applications in intelligent information processing, it has attracted much recent research attention. The classical support vector machines (SVM) algorithm has obtained significant success on this task. Inspired by the achievements of SVM, a family of related kernel...
A novel multiple classifiers fusion approach based on SFLS (Shortest Feature Line Segment) is proposed in this paper. SFLS is a kind of simple yet effective classification method depending on the shortest feature line. The original form of SFLS's output is just class label. To use SFLS as the member classifier in the multiple classifier system, the form of SFLS's output is modeled using the membership...
k nearest neighbor algorithm is a widely used classifier. It benefits from distances among features to classify the data. Classifiers based on distance metrics are affected from irrelevant or redundant features. Especially, it is valid for big datasets. So, some of features can be weighted with higher coefficients to reduce the effect of irrelevant or redundant features. We suggest adaptive weighted...
The aim of this paper is to show that an accurate and efficient text classifier for relatively simple problem domains can be created in only a few hours of development time. The motivating example discussed in the paper is a recent HackerRank competition problem that tasked competitors with creating a classifier for questions from the popular question and answer platform StackExchange. The paper describes...
To explore the academic progression of the students, higher educational institutions need better assessment and prediction tools. In this regard, Multilayer Perceptron (MLP) based prediction application is proposed to predict the Grade Point Average (GPA) of the Undergraduate students by the make use of student's Previous Academic History, Regularity, No. of Backlogs, Degree of Intelligence, Working...
Diagnosis of the thyroid function abnormalities may take much precious time of the patient. So, a computer aided diagnosis system can guide physicians in diagnosis and can save time of the patient. In this study, seven different types of neural networks were implemented in order to realize more robust and reliable networks on thyroid diagnosis. The particle swarm optimization and artificial bee colony...
Intrusion Detection System (IDS) has increasingly become a crucial issue for computer and network systems. Intrusion poses a serious security risk in a network environment. The ever growing new intrusion types pose a serious problem for their detection. The acceptability and usability of Intrusion Detection Systems get seriously affected with the data in network traffic. A large number of false alarms...
Electroencephalograph (EEG) based BCI (Brain-Computer Interfacing) paradigm has become a research topic in computer science and, in particular, human-computer interaction. In this paradigm, BCI applications have been largely focused on improving the quality of life for both able-bodied users as well as users with special needs. Despite the fact that low-cost off-the-shelf EEG headsets are becoming...
A transcription of each word can either be produced by rules, statistical models, or retrieved from dictionary. However, the lack of standards and the variation of how a Thai person romanizes his or her name pose transcription a challenging task. Although the dictionary-based approach seems to produce the most accurate result, a letter-to-sound conversion module is necessary for unknown names. We...
Our research concentrates on anomaly detection techniques, which have both industrial applications such as network monitoring and protection, as well as research applications such as software behavioral analysis or malware classification. During our doctoral research, we worked on anomaly detection from three different perspective, as a complex computer infrastructure has several weak spots that must...
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