Yasser F. Hassan

Rough sets for adapting wavelet neural networks as a new classifier system

Classification is an important theme in data mining. Rough sets and neural networks are two techniques applied to data mining problems. Wavelet neural networks have recently attracted great interest because of their advantages over conventional neural networks as they are universal approximations and achieve faster convergence. This paper presents a hybrid system to extract efficiently classification rules from decision table. The neurons of such hybrid network instantiate approximate reasoning knowledge gleaned from input data. The new model uses rough set theory to help in decreasing the computational effort needed for building the network structure by using what is called reduct algorithm and a rules set (knowledge) is generated from the decision table. By applying the wavelets, frequencies analysis, rough sets and dynamic scaling in connection with neural network, novel and reliable classifier architecture is obtained and its effectiveness is verified by the experiments comparing with traditional rough set and neural networks approaches.

Rough set classification based on quantum logic

Abstract By combining the advantages of quantum computing and soft computing, the paper shows that rough sets can be used with quantum logic for classification and recognition systems. We suggest the new definition of rough set theory as quantum logic theory. Rough approximations are essential elements in rough set theory, the quantum rough set model for set-valued data directly construct set approximation based on a kind of quantum similarity relation which is presented here. Theoretical analyses demonstrate that the new model for quantum rough sets has new type of decision rule with less redundancy which can be used to give accurate classification using principles of quantum superposition and non-linear quantum relations. To our knowledge, this is the first attempt aiming to define rough sets in representation of a quantum rather than logic or sets. The experiments on data-sets have demonstrated that the proposed model is more accuracy than the traditional rough sets in terms of finding optimal classifications.

K3. A region growing liver segmentation method with advanced morphological enhancement

Liver cancer (Hepatocellular carcinoma (HCC)) is considered one of the life threatening diseases that causes death. Early detection of HCC is considered as alive saving process. One of the important tools that helps in the early detection of HCC is image processing. This paper introduces a new size selection region growing based segmentation algorithm for highly accurate liver extraction from CT images. Our work was done in two main steps. We began by preprocessing the data set that we are going to start our segmentation process with. The data set consists of 69 case studies of different patients, among these studies we concentrate on 10 cases of different patients, liver positions, normal and up normal liver tissues. The second stage was extracting the liver from the CT images which was done in three consecutive steps thresholding, region growing and size selection morphological operations.

Semantic Clustering of Search Engine Results

This paper presents a novel approach for search engine results clustering that relies on the semantics of the retrieved documents rather than the terms in those documents. The proposed approach takes into consideration both lexical and semantics similarities among documents and applies activation spreading technique in order to generate semantically meaningful clusters. This approach allows documents that are semantically similar to be clustered together rather than clustering documents based on similar terms. A prototype is implemented and several experiments are conducted to test the prospered solution. The result of the experiment confirmed that the proposed solution achieves remarkable results in terms of precision.

Philosophy of gustatory perception strategy for hand gesture recognition

Purpose – Nature is the single and most complex system that has been always studied, and no one can compete Mother Nature, but we can learn from her, by many new methodologies through biology. The paper aims to discuss this issue. Design/methodology/approach – In this paper, being inspired by the mechanism through which our Mother Nature handling human taste, a proposed model for clustering and classification hand gesture is introduced based on human taste controlling strategy. Findings – The model can extract information from measurement data and handling it as the structure of tongue and the nervous systems of human taste recognition. Originality/value – The efficiency of proposed model is demonstrated experimentally on classifying the sign language data set; in the high recognition accuracy obtained for numbers of ASL was 95.52 percent.

NEW MODEL BASED ON CELLULAR AUTOMATA AND MULTIAGENT TECHNIQUES

The need for intelligent systems has grown in the past decade because of the increasing demand on humans and machines to perform better. The researchers of artificial intelligence (AI) have responded to these needs with the development of intelligent hybrid systems. This paper describes the modeling language for interacting hybrid systems in which we will build a new hybrid model of cellular automata and multiagent technology. Simulations with complex behavior will be model social dynamics where the focus is on the emergence of properties of local interactions. Therefore, in our approach, cellular automata form a useful framework for the multiagent simulation model and the model will be used for traffic system which lies in coordinating the local behavior of individual agent to provide an appropriate system-level behavior in grid of interacting organisms.

Cross-Lingual Ontology Enrichment Based on Multi-Agent Architecture

Abstract The proliferation of ontologies and multilingual data available on the Web has motivated many researchers to contribute to multilingual and cross-lingual ontology enrichment. Cross-lingual ontology enrichment greatly facilitates ontology learning from multilingual text/ontologies in order to support collaborative ontology engineering process. This article proposes a cross-lingual ontology enrichment (CLOE) approach based on a multi-agent architecture in order to enrich ontologies from a multilingual text or ontology. This has several advantages: 1) an ontology is used to enrich another one, written in a different natural language, and 2) several ontologies could be enriched at the same time using a single chunk of text (Simultaneous Ontology Enrichment). A prototype for the proposed approach has been implemented in order to enrich several ontologies using English, Arabic and German text. Evaluation results are promising and showing that CLOE performs well in comparison with four state-of-the-art approaches.

The problem learning Non-Taxonomic Relationships of Ontologies from unstructured data sources

In recent years, ontologies as a semantic knowledge representation become widely used in many information systems. Manual creation of ontologies by domain experts and ontology developers is also a costly task, time consuming and needs extra efforts. Learning Non-Taxonomic Relationships is a subfield of ontology learning which targets automatic extraction of non-taxonomic relationships from input, mostly unstructured, data sources and add them into its proper position in the ontology. This paper presents a discussion of the main process of learning Non-Taxonomic Relationships of Ontologies (LNTRO) from unstructured data source as well as corpora and web documents. We addressed the main tasks of LNTRO, the output of each task and techniques used. In addition, a set of state-of-the-art tools for learning non-taxonomic relations are presented. Finally, five approached representing the state of the art of Learning Non-Taxonomic Relationships of Ontologies are described along with their positive and negative aspects.