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This project explored fundamental methods to find the factors that can be used in classifying and detecting the type of wood. Whereas, the literatures have been reviewed to determine the algorithms developed. Some experiments have been conducted to analyze the model and system. The experiments are based on artificial neural network (ANN) algorithm that used back propagation and conjugate gradient...
Roller element bearing fault diagnosis is crucial in industry to maintain that the machine is in good condition so that there is no delay of work due to machine breakdown. This paper discusses the use of Extreme Learning Machine (ELM) algorithm to classify bearing faults. The performance of ELM is compared with Back Propagation (BP) algorithm. It was found that the results show that the ELM has smaller...
With the development of cloud computing technology, there are many scientists who want to perform their experiments in cloud environments. Because of the pay-per-use method, it is cost-optimal for scientists to only pay for the cloud services needed for their experiments. However, selection of suitable resources is difficult because they are composed of various characteristics. Therefore, a method...
Creativity is considered as a very important element of the society development. Having entered the big data era, people have been focusing on finding a pathway developing creativity for all applications. In the psychology and education domain, various approaches have been attempted, such as divergent thinking and brain storming. The psychology domain has researched on human's cognitive development...
The paper presents a deep analysis of the literature on the problems of optimization of parameters and the structure of the neural networks and the basic disadvantages that are present in the observed algorithms and methods. As a result, there are suggested a new algorithm for neural network structure optimization, which is devoided of the major shortcomings of other algorithms. The paper includes...
In this work, we present a performance comparison of the Multi Layer Perceptron (MLP), Support Vector Machines (SVM) and Voted Perceptron (VP) when applied to a social signal processing task. The signal processing task is in the field of computational politics where the aim is to predict the political parties of American congress members based on their response to certain questions. Using this dataset...
For deep learning applications, large numbers of samples are essential. If this condition is not met, effective features cannot be generated and overfitting occurs especially for the small datasets such as in medical applications. To address this issue, we propose a new dynamic ensemble merging algorithm that iteratively adjusts the weights of a convolutional neural network (CNN) ensemble's elements...
Sparse representation is a signal processing method which is mostly used in signal compression, noise reduction, and signal and image restoration fields. In this study, sparse representation was used in a different way from the traditional methods. In the proposed method, a hybrid structure was created by combining dictionary learning and ensemble classifier AdaBoost algorithms. The main idea of this...
Recent meta-learning approaches are oriented towards algorithm selection, optimization or recommendation of existing algorithms. In this paper we show how Inductive algorithms constructed from building blocks on small data sub-sample can be scaled up to model large data sets. We demonstrate how one particular template (simple ensemble of fast sigmoidal regression models) outperforms state-of-the-art...
A technique and algorithms for early detection of the started attack and subsequent blocking of malicious traffic are proposed. The primary separation of mixed traffic into trustworthy and malicious traffic was carried out using cluster analysis. Classification of newly arrived requests was done using different classifiers with the help of received training samples and developed success criteria.
Recently, multi-label classification has gained prime importance among the classification problems. The applications of classification problems has increased so rapidly that the need for efficient and accurate classifiers has become a vital requirement in the area of data mining. Multi-label classification problem is distinguished from the single label classification because of the capability to handle...
In traditional text sentiment analysis methods, text feature vector has the problem of high dimensionality and high sparseness. In view of this situation, we can cluster the similar words together and use the generated clusters to fit into a new dimension so that the text feature vector dimension will be decreased. By using Word2Vec tool and K-means clustering algorithm, this task can be completed...
Nowadays with the rapid development of network-based services and users of the internet in everyday life, intrusion detection becomes a promising area of research in the domain of security. Intrusion detection system (IDS) can detect the intrusions of someone who is not authorized to the present computer system automatically, so intrusion detection system has emerged as an essential component and...
Authors can be differentiated by their styles of writing. In this paper, we propose features which attempt to classify authors based on their writing styles. The features can be usage of parts of speech, punctuation marks, word lengths, sentence lengths, number of unique words used, etc. This concept is used in many fields like email classification, fraud detection, etc. We propose a module to extract...
We present an optimization technique for general object detection and an algorithm for training decision trees. By delaying the calculation of the features as late as possible we drastically reduce the execution time. At detection we alternate between evaluating the necessary features and eliminating candidates. This enables us to have both a rich pool of features and a powerful classifier while keeping...
Graduate employability is an increasingly major concern for academic institutions and assessing student employability provides a way of linking student skills and employer business requirements. Enhancing student assessment methods for employability can improve their understanding about companies in order to get suitable company for them. So, enhanced employability prediction of student can help them...
Tagging provides a convenient means to assign tokens of identification to research papers which facilitate recommendation, search and disposition process of research papers. This paper contributes a document centered approach for auto-tagging of research papers. The auto-tagging method mainly comprises of two processes:- classification and tag selection. The classification process involves automatic...
In the Linked Data context, identity link is one of the most important semantic links that can be established between the datasets. It specifies that different identifiers refer to the same real world object and therefore must be linked. The process of detecting these identical instances across different data repositories is referred as instance matching. This is used to connect existing data sources...
This paper focuses on the problem of machine learning classifier choice for network intrusion detection, taking into consideration several ensemble classifiers from the supervised learning category. We have evaluated Bagged trees, AdaBoost, RUSBoost, LogitBoost and GentleBoost algorithms, provided an analysis of the performance of the classifiers and compared their learning capabilities, taking for...
Flight parameters record the flight state and performance of the each flight phase. The precise division of the aircraft flight process using flight parameters can not only perform the stage quality evaluation of the whole flight process, but also can detect the aircraft faults. In this paper, the decision tree classifier is used to divide the flight parameters. The parameter reduction is carried...
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