Hesham Hassan

TextOntoEx: Automatic ontology construction from natural English text

Most of existing ontologies construction tools support construction of ontological relations (e.g., taxonomy, equivalence, etc.) but they do not support construction of domain relations, non-taxonomic conceptual relationships (e.g., causes, caused by, treat, treated by, has-member, contain, material-of, operated-by, controls, etc.). Domain relations are found mainly in text sources. TextOntoEx constructs ontology from natural domain text using semantic pattern-based approach. TextOntoEx is a chain between linguistic analysis and ontology engineering. TextOntoEx analyses natural domain text to extract candidate relations and then maps them into meaning representation to facilitate constructing ontology. The paper explains this approach in more details and discusses some experiments on deriving ontology from natural text.

A Case Study for Deploying Applications on Heterogeneous PaaS Platforms

Cloud Platform-as-a-Service (PaaS) model provides developers with the ability to deploy and manage their applications remotely through the cloud and pay only for actual usage hours. Currently, there is no standard API for PaaS management and deployment, each PaaS provider has its own specific APIs (e.g., Google AppEngine (GAE), OpenShift (OS), Cloud Foundry (CF), and Windows Azure). Therefore, deploying applications on heterogeneous PaaS platforms is considered one of the challenges that make some developers worry about using PaaS services. Such challenge can be solved by providing a standard or a generic API that overcomes PaaS API heterogeneity. The aim of this paper is to report on our effort to use and extend a generic API, namely the COAPS API, which supports deployment and management on Cloud Foundry and OpenShift. According to the work in this paper, an extension of the COAPS API is provided to include the deployment on Google AppEngine as a case study to demonstrate COAPS API generality.

A novel approach for measuring hyperspectral similarity

Hyperspectral measures are used to capture the degree of similarity between two spectra. Spectral angle mapper (SAM) is an example of such measures. SAM similarity values range from 0 to 1. These values do not indicate whether the two spectra are similar or not. A static similarity threshold is imposed to recognize similar and dissimilar spectra. Adjusting such threshold is a troublesome process. To overcome this problem, the proposed approach aims to develop learnable hyperspectral measures. This is done through using hyperspectral measures values as similarity patterns and employing a classifier. The classifier acts as an adaptive similarity threshold. The derived similarity patterns are flexible, as they are able to capture the specific notion of similarity that is appropriate for each spectral region. Two similarity patterns are proposed. The first pattern is the cosine similarity vector for the second spectral derivative pair. The second pattern is a composite vector of different similarity measures values. The proposed approach is applied on full hyperspectral space and subspaces. Experiments were conducted on a challenging benchmark dataset. Experimental results showed that, classifications based on second patterns were far better than first patterns. This is because first patterns were concerned only with the geometrical features of the spectral signatures, while second patterns combined various discriminatory features such as: orthogonal projections information, correlation coefficients, and probability distributions produced by the spectral signatures. The proposed approach results are statistically significant. This implies that using simple learnable measures outperforms complex and manually tuned techniques used in classification.

Development and implementation of a knowledge acquisition methodology for crop management expert systems

Abstract This paper presents methodology developed for knowledge acquisition for crop management expert systems. The proposed methodology is described through an extended waterfall model for knowledge acquisition. The way in which the methodology was implemented is presented, and the experience gained is discussed. Although the methodology has evolved through the development of an expert system for cucumber seedling production, it can be used for other crops. A field prototype of this expert system was implemented and is currently being tested in a real environment.

MCDM method based on improved fuzzy decision map

In this paper, we improve the Fuzzy Decision Map (FDM) by using linguistic values rather than crisp membership values for the link weights (i.e. preference and causal relationships among criteria with fuzzy linguistic). The proposed method is called the linguistic fuzzy decision network. It provides both local fuzzy weights and global fuzzy weights. The proposed method is quite appropriate to decision makers to reflect the practical vagueness and imprecision existed for solving Multi-Criteria Decision-Making (MCDM) in real world situations. A case study is used to compare the performance of the proposed model with the original fuzzy decision maps model. The result of comparison ensures the ability to draw the same decisions with a more realistic decision environment.

On the significance of fuzzification of the N and m in cancer staging.

The tumor, node, metastasis (TNM) staging system has been regarded as one of the most widely used staging systems for solid cancer. The “T” is assigned a value according to the primary tumor size, whereas the “N” and “M” are dependent on the number of regional lymph nodes and the presence of distant metastasis, respectively. The current TNM model classifies stages into five crisp classes. This is unrealistic since the drastic modification in treatment that is based on a change in one class may be based on a slight shift around the class boundary. Moreover, the system considers any tumor that has distant metastasis as stage 4, disregarding the metastatic lesion concentration and size. We had handled the problem of T staging in previous studies using fuzzy logic. In this study, we focus on the fuzzification of N and M staging for more accurate and realistic modeling which may, in turn, lead to better treatment and medical decisions.

A Multi-Level Linguistic Fuzzy Decision Network Hierarchical Structure Model for Crop Selection

Cultivate the best crop from many suitable crops is a complex process that faces the decision makers (e.g. farmers, their advisors, and others in the agricultural sector). Their goal is to select a crop which maximizes the resource utilization and in the same time ensures the sustainability for natural agricultural resources. Selecting such crop for cultivating among many suitable alternatives crops is a Multiple Criteria Decision Making (MCDM) problem. Since, the selection for the best decision is dependent in many criteria and having dependence and feedback among them. In this paper Linguistic Fuzzy Decision Network (LFDN) method is developed and applied to a real case study to decide the cultivate crop among four crops-namely: Wheat, Corn, Rice, and Fababean w.r.t given multiple criteria.

Multi-level Linguistic Fuzzy Decision Network Hierarchical Structure Model for MCDM

Linguistic Fuzzy Decision network (LFDN) method is an extension of Fuzzy Decision Map (FDM) for solving Multi-Criteria Decision Making problems (MCDM) in fuzzy environment having dependence and feedback among criteria. On the other hand, LFDN can’t handle the complex decision making problem, particularly with the multi-level hierarchical structure model that consists of objectives, criteria, sub-criteria, etc. down to the bottom level (alternatives). The main objective of this paper is to develop the LFDN structure to be able to select a decision for multi-level structure problems. So the multi-level structure of LFDN is the general form of LFDN. Therefore, it can use for ranking alternatives and selecting the best one when the decision maker has multiple criteria. A case study was carried out to demonstrate the proposed model.

Power Aware Computing Survey

aware computing has caught the interest of researchers and users of all computing systems. In embedded systems and small devices, better management of energy translates into longer lasting and smaller batteries, which in turn implies smaller and lighter devices. In cloud, distributed, and high performance computing systems, better management of power translates into saving a significant amount of money and natural resources. This paper surveys the different power- aware computing approaches and techniques, focusing mostly on software approaches. It also introduces power-aware computing and why it is very important these days. The paper discusses the ways and challenges of measuring the energy consumption of systems and devices.

Web Service Composition and Legacy Systems: A Survey

Service Oriented Architecture (SOA) has gained considerable interest in recent years, mostly due to the advent of standards based Web services that simplify interoperability, loose coupling and reuse. One of the basic business motivations for implementing SOA today is achieving business agility, as SOA can help businesses respond more quickly and cost effectively to the dynamic and continues changes in market conditions. It can also simplify interconnection to the existing legacy systems as well as reconfiguring loosely coupled business services in a simple, fast and low cost manner. For SOA to succeed in that, it is a key issue to provide a Web service composition approach to facilitate business innovation and adapt IT to todays fast changing markets. In this paper, we present a survey of some existing proposals about service composition approaches and provide an overview of the strategies for the modernization of the legacy system using SOA.