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During the last few years, the search result clustering has attracted a substantial amount of research. In this paper, we present a comparative study of the performance of fuzzy clustering algorithms, namely Fuzzy C-Means (FCM), and Gustafson-Kessel (GK) algorithms with clustering search results. Therefore, there is a need to reduce the information, help filtering out irrelevant items, and favors...
Dimensionality reduction is an essential task for many large-scale information processing problems such as classifying document sets, searching over Web data sets, etc. It can be used to improve both the efficiency and the effectiveness of classifiers. In this paper, a comparative study is conducted of five Dimension Reduction Techniques in the context of the Arabic text classification problem using...
Today the movie classification also known as“Movie Rating” still does not has the computerized systems to support and still using human help to classify as a censorship. This movie classification process also takes long time and may have some conflicts among the committee because of their background and experience. The committee also needs to use the vote for helping then consents all their conflicts...
In many application domains such as information retrieval, computational biology, and image processing the data dimension is usually very high. Developing effective clustering methods for high dimensional dataset is a challenging problem due to the curse of dimensionality. The k-means clustering algorithm is used for many practical applications. But it is computationally expensive and the quality...
This paper proposes a new strategy that is based on the signal processing tools applied to text compression of files namely, the wavelet transform and the fourier transform. The influence of compression size and threshold of wavelet filters and the fourier transform as well as two parameters: families of wavelet filters and decomposition levels, on compression factor of text files are investigated...
This paper presents the results of an experimental study of some similarity measures used for both Information Retrieval and Document Clustering. Our results indicate that the cosine similarity measure is superior than the other measures such as Jaccard measure, Euclidean measure that we tested. Cosine Similarity measure is particularly better for text documents. Previously these measures are compared...
Word Sense Disambiguation (WSD) is main task in the area of natural language processing (NLP). Supervised WSD methods are shown to be more effective than other WSD methods with the limitation of the size of manual annotated learning set. On the other hand, Concept graph is a weighted graph with each of its edges representing the relationships between concepts (relevancy of each pair of concepts)....
Most of the algorithms of data compression were developed for English language. However, the aptitude of wavelet transform to be multilingual lossy text compression is promised. This paper proposes a new strategy that is based on the wavelet transform applied to text compression of files. The influences of two parameters, namely families of wavelet filters and decomposition levels, on compression...
The γ' precipitate size of IN738LC is predicted using a Levenberg-Marquard backpropagation neural network in matlab toolbox. A cast polycrystalline Ni based super alloy IN738LC (a gas turbine material) is considered and the γ' precipitate size is described as a function of 5 variables (solutionizing temperature, solutionizing duration, ageing temperature, ageing duration, and cooling method (furnace...
This paper describes an isolated word recognition method based on distinctive phonetic features (DPFs). The method comprises two multilayer neural networks (MLNs). The first MLN, MLNLF-DPF, maps local features (LFs) of an input speech signal into discrete DPFs and the second MLN, MLNDyn, restricts dynamics of outputted DPFs by the MLNLF-DPF. In the experiments on Tohokudai Isolated Spoken-Word Database...
Face Recognition is the process of identification of a person by his facial image. As applied to face recognition, this paper proposes a method, comprising of Laplacian of Gaussian (LoG) filter for intricate facial detail enhancement, Singular Value Decomposition (SVD) for holistic feature extraction and Feed forward Neural Network (FFNN) for classification. Applications of LoG filter highlights,...
In this paper we present how we combine artificial intelligence and knowledge engineering to provide an ontology inspired approach in order to manage a virtual environment for risk prevention. In the virtual environment, different entities cohabit: virtual operators represented by cognitive agents and the learner's avatar that represents a real operator. They can interact with the objects through...
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