Farid Meziane

Intelligent systems in manufacturing: current developments and future prospects

Global competition and rapidly changing customer requirements are demanding increasing changes in manufacturing environments. Enterprises are required to constantly redesign their products and continuously reconfigure their manufacturing systems. Traditional approaches to manufacturing systems do not fully satisfy this new situation. Many authors have proposed that artificial intelligence (AI) will bring the flexibility and efficiency needed by manufacturing systems. This paper is a review of AI techniques used in manufacturing systems. The paper first defines the components of a simplified intelligent manufacturing systems (IMS), the different AI techniques to be considered and then shows how these AI techniques are used for the components of IMS.

Generating Natural Language specifications from UML class diagrams

Early phases of software development are known to be problematic, difficult to manage and errors occurring during these phases are expensive to correct. Many systems have been developed to aid the transition from informal Natural Language requirements to semi-structured or formal specifications. Furthermore, consistency checking is seen by many software engineers as the solution to reduce the number of errors occurring during the software development life cycle and allow early verification and validation of software systems. However, this is confined to the models developed during analysis and design and fails to include the early Natural Language requirements. This excludes proper user involvement and creates a gap between the original requirements and the updated and modified models and implementations of the system. To improve this process, we propose a system that generates Natural Language specifications from UML class diagrams. We first investigate the variation of the input language used in naming the components of a class diagram based on the study of a large number of examples from the literature and then develop rules for removing ambiguities in the subset of Natural Language used within UML. We use WordNet, a linguistic ontology, to disambiguate the lexical structures of the UML string names and generate semantically sound sentences. Our system is developed in Java and is tested on an independent though academic case study.

From English to formal specifications

Specifications provide the foundation upon which a system can be formally developed. If a specification is wrong, then no matter what method of design is used, or what quality assurance procedures are in place, they will nnot result in a system that meets the requirements. nThe specification of a system involves people of different profiles who favour different representations. At the beginning natural language is used because the specification document acts as a contract between the user and the developers. Most of the time, the only representation that users understand and agree on is natural language. At the other end, developers find nnatural language specifications ambiguous and incomplete and may therefore prefer formal specifications. The transition from informal specifications to formal ones is an error prone and time consuming process. This transition nmust be supported to ensure that the formal specifications are consistent with the informal ones. nIn this research we propose an interactive approach for producing formal specifications from English specifications. The approach uses research in the area of natural language understanding to analyse English specifications in order to detect ambiguities. The method used for analysing natural language text is based on McCord’s approach. This method consists of ntranslating natural language sentences into a logical form language representation. nThis helps to identify ambiguities present in natural language specifications and to identify the entities and relationships. These entities and relationships are used as a basis for producing VDM data types. nWe also investigate the production of data type invariants for restricted sentences and the production of some common specifications. nWe test our approach by implementing it in Prolog-2 and apply it to an independent case study.

A fuzzy trust model for e-commerce

It is argued that e-commerce has not reached its full potential and trust was often cited as the main reason why many customers are still skeptical about some online vendors. Many trust models have been developed, but most are subjective and did not take into account the vagueness and ambiguity of the domain and the specificity of customers. We have developed a model that attempts to identify the information customers expect to find on a vendors website to increase their trust and hence the likelihood of a transaction to take place. In this paper, we present a method based on fuzzy logic to evaluate trust in e-commerce. We argue that fuzzy logic is suitable for trust evaluation as it takes into account the uncertainties within e-commerce data and like human relationships, trust is often expressed by linguistics terms rather then numerical values. We validated the system using two case studies.

A document management methodology based on similarity contents

The advent of the WWW and distributed information systems have made it possible to share documents between different users and organisations. However, this has created many problems related to the security, accessibility, right and most importantly the consistency of documents. It is important that the people involved in the documents management process have access to the most up-to-date version of documents, retrieve the correct documents and should be able to update the documents repository in such a way that his or her document are known to others. In this paper we propose a method for organising, storing and retrieving documents based on similarity contents. The method uses techniques based on information retrieval, document indexation and term extraction and indexing. This methodology is developed for the E-Cognos project which aims at developing tools for the management and sharing of documents in the construction domain.

An information framework for a merchant trust agent in electronic commerce

Data Mining.- Mining Frequent Sequential Patterns under a Similarity Constraint.- Pre-pruning Classification Trees to Reduce Overfitting in Noisy Domains.- Data Mining for Fuzzy Decision Tree Structure with a Genetic Program.- Co-evolutionary Data Mining to Discover Rules for Fuzzy Resource Management.- Discovering Temporal Rules from Temporally Ordered Data.- Automated Personalisation of Internet Users Using Self-Organising Maps.- Data Abstractions for Numerical Attributes in Data Mining.- Calculating Aggregates with Range-Encoded Bit-Sliced Index.- T3: A Classification Algorithm for Data Mining.- A Hierarchical Model to Support Kansei Mining Process.- Evolving SQL Queries for Data Mining.- Indexing and Mining of the Local Patterns in Sequence Database.- Knowledge Engineering.- A Knowledge Discovery by Fuzzy Rule Based Hopfield Network.- Fusing Partially Inconsistent Expert and Learnt Knowledge in Uncertain Hierarchies.- Organisational Information Management and Knowledge Discovery in Email within Mailing Lists.- Design of Multi-drilling Gear Machines by Knowledge Processing and Machine Simulation.- Text and Document Processing.- Classification of Email Queries by Topic: Approach Based on Hierarchically Structured Subject Domain.- A Knowledge-Based Information Extraction System for Semi-structured Labeled Documents.- Measuring Semantic Similarity Between Words Using Lexical Knowledge and Neural Networks.- Extraction of Hidden Semantics from Web Pages.- Self-Organising Maps for Hierarchical Tree View Document Clustering Using Contextual Information.- Schema Discovery of the Semi-structured and Hierarchical Data.- RSTIndex: Indexing and Retrieving Web Document Using Computational and Linguistic Techniques.- A Case-Based Recognition of Semantic Structures in HTML Documents.- Expeditious XML Processing.- Document Clustering Using the 1 + 1 Dimensional Self-Organising Map.- Natural Language Processing for Expertise Modelling in E-mail Communication.- Internet Applications.- A Branch and Bound Algorithm for Minimum Cost Network Flow Problem.- Study of the Regularity of the Users Internet Accesses.- An Intelligent Mobile Commerce System with Dynamic Contents Builder and Mobile Products Browser.- Focused Crawling Using Fictitious Play.- A User Adaptive Mobile Commerce System with a Middlet Application.- Weight-Vector Based Approach for Product Recommendation in E-commerce.- The Development of an XML-Based Data Warehouse System.- Identifying Data Sources for Data Warehouses.- Agent Technologies.- Coordinating Learning Agents via Utility Assignment.- AGILE: An Agent-Assisted Infrastructure to Support Learning Environments.- Multi-agent Fuzzy Logic Resource Manager.- Transactional Multiple Agents.- An Information Model for a Merchant Trust Agent in Electronic Commerce.- MASIVE: A Case Study in Multiagent Systems.- Learning Multi-agent Strategies in Multi-stage Collaborative Games.- Emergent Specialization in Swarm Systems.- Distributed Mobile Communication Base Station Diagnosis and Monitoring Using Multi-agents.- ABBA - Agent Based Beaver Application - Busy Beaver in Swarm.- Centralised and Distributed Organisational Control.- Special Session on Autonomous Mining.- Mining Dependence Structures from Statistical Learning Perspective.- k*-Means -Data Mining.- Mining Frequent Sequential Patterns under a Similarity Constraint.- Pre-pruning Classification Trees to Reduce Overfitting in Noisy Domains.- Data Mining for Fuzzy Decision Tree Structure with a Genetic Program.- Co-evolutionary Data Mining to Discover Rules for Fuzzy Resource Management.- Discovering Temporal Rules from Temporally Ordered Data.- Automated Personalisation of Internet Users Using Self-Organising Maps.- Data Abstractions for Numerical Attributes in Data Mining.- Calculating Aggregates with Range-Encoded Bit-Sliced Index.- T3: A Classification Algorithm for Data Mining.- A Hierarchical Model to Support Kansei Mining Process.- Evolving SQL Queries for Data Mining.- Indexing and Mining of the Local Patterns in Sequence Database.- Knowledge Engineering.- A Knowledge Discovery by Fuzzy Rule Based Hopfield Network.- Fusing Partially Inconsistent Expert and Learnt Knowledge in Uncertain Hierarchies.- Organisational Information Management and Knowledge Discovery in Email within Mailing Lists.- Design of Multi-drilling Gear Machines by Knowledge Processing and Machine Simulation.- Text and Document Processing.- Classification of Email Queries by Topic: Approach Based on Hierarchically Structured Subject Domain.- A Knowledge-Based Information Extraction System for Semi-structured Labeled Documents.- Measuring Semantic Similarity Between Words Using Lexical Knowledge and Neural Networks.- Extraction of Hidden Semantics from Web Pages.- Self-Organising Maps for Hierarchical Tree View Document Clustering Using Contextual Information.- Schema Discovery of the Semi-structured and Hierarchical Data.- RSTIndex: Indexing and Retrieving Web Document Using Computational and Linguistic Techniques.- A Case-Based Recognition of Semantic Structures in HTML Documents.- Expeditious XML Processing.- Document Clustering Using the 1 + 1 Dimensional Self-Organising Map.- Natural Language Processing for Expertise Modelling in E-mail Communication.- Internet Applications.- A Branch and Bound Algorithm for Minimum Cost Network Flow Problem.- Study of the Regularity of the Users Internet Accesses.- An Intelligent Mobile Commerce System with Dynamic Contents Builder and Mobile Products Browser.- Focused Crawling Using Fictitious Play.- A User Adaptive Mobile Commerce System with a Middlet Application.- Weight-Vector Based Approach for Product Recommendation in E-commerce.- The Development of an XML-Based Data Warehouse System.- Identifying Data Sources for Data Warehouses.- Agent Technologies.- Coordinating Learning Agents via Utility Assignment.- AGILE: An Agent-Assisted Infrastructure to Support Learning Environments.- Multi-agent Fuzzy Logic Resource Manager.- Transactional Multiple Agents.- An Information Model for a Merchant Trust Agent in Electronic Commerce.- MASIVE: A Case Study in Multiagent Systems.- Learning Multi-agent Strategies in Multi-stage Collaborative Games.- Emergent Specialization in Swarm Systems.- Distributed Mobile Communication Base Station Diagnosis and Monitoring Using Multi-agents.- ABBA - Agent Based Beaver Application - Busy Beaver in Swarm.- Centralised and Distributed Organisational Control.- Special Session on Autonomous Mining.- Mining Dependence Structures from Statistical Learning Perspective.- k*-Means - A Generalized k-Means Clustering Algorithm with Unknown Cluster Number.- Multiagent SAT (MASSAT): Autonomous Pattern Search in Constrained Domains.- A Text Mining Agents Based Architecture for Personal E-mail Filtering and Management.- Framework of a Multi-agent KDD System.- Financial Engineering.- Intraday FX Trading: An Evolutionary Reinforcement Learning Approach.- An Up-Trend Detection Using an Auto-Associative Neural Network: KOSPI200 Futures.- Stock Price and Index Forecasting by Arbitrage Pricing Theory-Based Gaussian TFA Learning.- A Comparative Study on Three MAP Factor Estimate Approaches for NFA.- A Neural Classifier with Fraud Density Map for Effective Credit Card Fraud Detection.- A Comparison of Two Techniques for Next- Day Electricity Price Forecasting.- Support Vector Machine Regression for Volatile Stock Market Prediction.- Complexity Pursuit for Financial Prediction.- Artificial Intelligence in Portfolio Management.- The Multilevel Classification Problem and a Monotonicity Hint.- Adaptive Filtering for GARCH Models.- Bio-Informatics.- Application of Self-Organising Maps in Automated Chemical Shift Correction of In Vivo 1H MR Spectra.- Supervised Learning of Term Similarities.- BIKMAS: A Knowledge Engineering System for Bioinformatics.- Unsupervised Feature Extraction of in vivo Magnetic Resonance Spectra of Brain Tumours Using Independent Component Analysis.- Fuzzy Rule-Based Framework for Medical Record Validation.- Learning Systems.- Classification Learning by Decomposition of Numerical Datasets.- Combining Feature Selection with Feature Weighting for k-NN Classifier.- Pattern Selection for Support Vector Classifiers.- Graphical Features Selection Method.- Fuzzy-Neural Inference in Decision Trees.- Decision Tree Based Clustering.- Usage of New Information Estimations for Induction of Fuzzy Decision Trees.- Genetic Algorithm Based-On the Quantum Probability Representation.- A Dynamic Method for Discretization of Continuous Attributes.- A New Neural Implementation of Exploratory Projection Pursuit.- A General Framework for a Principled Hierarchical Visualization of Multivariate Data.- Chinese Character Recognition-Comparison of Classification Methodologies.- Lempel-Ziv Coding in Reinforcement Learning.- Pattern Recognition.- Efficient Face Extraction Using Skin-Color Model and a Neural Network.- Feature Weights Determining of Pattern Classification by Using a Rough Genetic Algorithm with Fuzzy Similarity Measure.- Recursive Form of the Discrete Fourier Transform for Two-Dimensional Signals.- Viseme Recognition Experiment Using Context Dependent Hidden Markov Models.- Stave Extraction for Printed Music Scores.- Scaling-Up Model-Based Clustering Algorithm by Working on Clustering Features.- A New Approach to Hierarchically Retrieve MPEG Video.- Alpha-Beta Search Revisited.- Quantifying Relevance of Input Features.eCommerce is a faceless business arrangement where the process of creating trust towards merchants, hereby referred to as merchant trust, is still a big challenge. Merchant trust framework can be created by using factors such as existence, affiliation, performance and policy. In this paper, we present an information model for a merchant trust based on the previously cited factors. We then provide a framework for the implementation of the model using intelligent agents. Gathering the required information on the merchant is the first step in helping consumers to evaluate merchant trust in an eCommerce setting.

Evaluating trust in electronic commerce: a study based on the information provided on merchants’ websites

Lack of trust has been identified as a major problem hampering the growth of Electronic Commerce (EC). It is reported by many studies that a large number of online shoppers abandon their transactions because they do not trust the website when they are asked to provide personal information. To support trust, we developed an information framework model based on research on EC trust. The model is based on the information a consumer expects to find on an EC website and that is shown from the literature to increase his/her trust towards online merchants. An information extraction system is then developed to help the user find this information. In this paper, we present the development of the information extraction system and its evaluation. This is then followed by a study looking at the use of the identified variables on a sample of EC websites.

Artificial Intelligence Applications for Improved Software Engineering Development: New Prospects

Despite decades of research, developing software that is fit for purpose, developed on time, and within budget remains a challenge. Many researchers have advocated the use of artificial intelligence techniques such as knowledge-based systems, neural networks, and data mining as a way of addressing these difficulties. Artificial Intelligence Applications for Improved Software Engineering Development: New Prospects provides an overview of useful techniques in artificial intelligence for future software development along with critical assessment for further advancement. A compendium of latest industry findings, this Premier Reference Source offers researchers, academicians, and practitioners developmental ideas within the field.

Experience with mural in formalising Dust-Expert

Abstract The mural system was an outcome of a significant effort to develop a support tool for the effective use of a full formal methods development cycle. Experience with it, however, has been limited to a small number of illustrative examples that have been carried out by those closely associated with its development and implementation. This paper aims to remedy this situation by describing the experience of using mural for specifying Dust-Expert, an expert system for the relief venting of dust explosions in chemical processes. The paper begins by summarising the main requirements for Dust-Expert, and then gives a flavour of the VDM specification that was formalised using mural . The experience of using mural is described with respect to users expectations that a formal methods tool should: (i) spot any inconsistencies; (ii) help manage and organise the specifications and allow one to easily add, access, update and delete specifications; (iii) help manage and carry out the refinement process; (iv) help manage and organise theories; (v) help manage and carry out proofs. The paper concludes by highlighting the strengths and weaknesses of mural that could be of interest to those developing the next generation of formal methods development tools.

Extracting person names from Arabic newspapers

Named Entity recognition is very new in the Arabic Language although it has reached the maturity stage for some other languages such as English and French. In this paper, we describe the development and implementation of a person name named entity recognition system for the Arabic Language. We use heuristics based on a set of keywords rather than complex grammars, statistical and machine learning techniques. However, the results obtained are of the same standards or better in some cases as those systems that are using more sophisticated approaches.