The notion of fuzziness as defined in this paper relates to situations in which the source of imprecision is not a random variable or a stochastic process, but rather a class or classes which do not possess sharply defined boundaries, e.g., the “class of bald men,” or the “class of numbers which are much greater than 10,” or the “class of adaptive systems,” etc. A basic concept which makes it possible to treat fuzziness in a quantitative manner is that of a fuzzy set, that is, a class in which there may be grades of membership intermediate between full membership and non-membership. Thus, a fuzzy set is characterized by a membership function which assigns to each object its grade of membership (a number lying between 0 and 1) in the fuzzy set. After a review of some of the relevant properties of fuzzy sets, the notions of a fuzzy system and a fuzzy class of systems are introduced and briefly analyzed. The paper closes with a section dealing with optimization under fuzzy constraints in which an approach to...

Fuzzy sets and systems

Object-Oriented Software Metrics.

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Service Oriented Architecture Concepts Technology And Design

Artificial Intelligence techniques: An introduction to their use for modelling environmental systems

https://cyberleninka.org/article/n/670180.pdf

Fuzzy TOPSIS: A General View

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.

TextOntoEx: Automatic ontology construction from natural English text

Background Writing composition is a significant factor for measuring test-takers' ability in any language exam. However, the assessment (scoring) of these writing compositions or essays is a very challenging process in terms of reliability and time. The need for objective and quick scores has raised the need for a computer system that can automatically grade essay questions targeting specific prompts. Automated Essay Scoring (AES) systems are used to overcome the challenges of scoring writing tasks by using Natural Language Processing (NLP) and machine learning techniques. The purpose of this paper is to review the literature for the AES systems used for grading the essay questions. Methodology We have reviewed the existing literature using Google Scholar, EBSCO and ERIC to search for the terms "AES", "Automated Essay Scoring", "Automated Essay Grading", or "Automatic Essay" for essays written in English language. Two categories have been identified: handcrafted features and automatically featured AES systems. The systems of the former category are closely bonded to the quality of the designed features. On the other hand, the systems of the latter category are based on the automatic learning of the features and relations between an essay and its score without any handcrafted features. We reviewed the systems of the two categories in terms of system primary focus, technique(s) used in the system, the need for training data, instructional application (feedback system), and the correlation between e-scores and human scores. The paper includes three main sections. First, we present a structured literature review of the available Handcrafted Features AES systems. Second, we present a structured literature review of the available Automatic Featuring AES systems. Finally, we draw a set of discussions and conclusions. Results AES models have been found to utilize a broad range of manually-tuned shallow and deep linguistic features. AES systems have many strengths in reducing labor-intensive marking activities, ensuring a consistent application of scoring criteria, and ensuring the objectivity of scoring. Although many techniques have been implemented to improve the AES systems, three primary challenges have been identified. The challenges are lacking of the sense of the rater as a person, the potential that the systems can be deceived into giving a lower or higher score to an essay than it deserves, and the limited ability to assess the creativity of the ideas and propositions and evaluate their practicality. Many techniques have only been used to address the first two challenges.

Automated language essay scoring systems: a literature review.

An extension of fuzzy decision maps for multi-criteria decision-making

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 Case Study for Deploying Applications on Heterogeneous PaaS Platforms

This paper presents a real-life pest control expert system for tomato. The system involves two main subtasks, namely: ‘diagnose’ and ‘treat’. The ‘diagnose’ subtask finds out the causes of the growers' complaints, while the ‘treat’ subtask finds out a treatment plan for these causes. CommonKADS methodology has been used to develop the system. Dependency network is used as one of our knowledge representation schemes in both subtasks. An expert system evaluation methodology has been suggested and applied to the developed system.

Hesham Hassan

Papers

TextOntoEx: Automatic ontology construction from natural English text

Automated language essay scoring systems: a literature review.

An extension of fuzzy decision maps for multi-criteria decision-making

A Case Study for Deploying Applications on Heterogeneous PaaS Platforms

Pest Control Expert System for Tomato (PCEST)