Feature Extraction Of Retinal Images Interfaced With A Rule-Based Expert System

Based on the authors previous work of developing an expert system for fault diagnosis of CNC machine tool, this paper studied the theory and method of CNC remote fault diagnosis expert system based on B/S, and presents schema and structure of the expert system in detailed. Case based reasoning is used for the multi-alarm diagnosis, and rule based reasoning is used for single-alarm diagnosis. At last fault diagnosis expert system was designed and developed making use of C# and ASP.NET.

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Mixed qualitative–quantitative approach for bidding decisions in construction

Engineering Construction & Architectural Management ◽

10.1108/ecam-12-2020-1060 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Emad Mohamed ◽

Parinaz Jafari ◽

Ahmed Hammad

Keyword(s):

Expert System ◽

Expert Knowledge ◽

The Body ◽

Decision Makers ◽

Quantitative Approach ◽

Rule Based ◽

Content Type ◽

Factors Affecting ◽

Qualitative And Quantitative ◽

Mixed Approach

PurposeThe bid/no-bid decision is critical to the success of construction contractors. The factors affecting the bid/no-bid decision are either qualitative or quantitative. Previous studies on modeling the bidding decision have not extensively focused on distinguishing qualitative and quantitative factors. Thus, the purpose of this paper is to improve the bidding decision in construction projects by developing tools that consider both qualitative and quantitative factors affecting the bidding decision.Design/methodology/approachThis study proposes a mixed qualitative-quantitative approach to deal with both qualitative and quantitative factors. The mixed qualitative-quantitative approach is developed by combining a rule-based expert system and fuzzy-based expert system. The rule-based expert system is used to evaluate the project based on qualitative factors and the fuzzy expert system is used to evaluate the project based on the quantitative factors in order to reach the comprehensive bid/no-bid decision.FindingsThree real bidding projects are used to investigate the applicability and functionality of the proposed mixed approach and are tested with experts of a construction company in Alberta, Canada. The results demonstrate that the mixed approach provides a more reliable, accurate and practical tool that can assist decision-makers involved in the bid/no-bid decision.Originality/valueThis study contributes theoretically to the body of knowledge by (1) proposing a novel approach capable of modeling all types of factors (either qualitative or quantitative) affecting the bidding decision, and (2) providing means to acquire, store and reuse expert knowledge. Practical contribution of this paper is to provide decision-makers with a comprehensive model that mimics the decision-making process and stores experts' knowledge in the form of rules. Therefore, the model reduces the administrative burden on the decision-makers, saves time and effort and reduces bias and human errors during the bidding process.

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Impact of techniques to reduce error in high error rule-based expert system gradient descent networks

Journal of Intelligent Information Systems ◽

10.1007/s10844-021-00672-7 ◽

2021 ◽

Author(s):

Jeremy Straub

Keyword(s):

Expert System ◽

Gradient Descent ◽

Rule Based

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A FULLY AUTOMATED OFFLINE HANDWRITING RECOGNITION SYSTEM INCORPORATING RULE BASED NEURAL NETWORK VALIDATED SEGMENTATION AND HYBRID NEURAL NETWORK CLASSIFIER

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001404003654 ◽

2004 ◽

Vol 18 (07) ◽

pp. 1267-1283 ◽

Cited By ~ 5

Author(s):

MOUMITA GHOSH ◽

RANADHIR GHOSH ◽

BRIJESH VERMA

Keyword(s):

Neural Network ◽

Feature Extraction ◽

Solution Method ◽

Handwriting Recognition ◽

Recognition System ◽

Neural Network Classifier ◽

Hybrid Neural Network ◽

Rule Based ◽

Extraction Algorithm ◽

Offline Handwriting Recognition

In this paper we propose a fully automated offline handwriting recognition system that incorporates rule based segmentation, contour based feature extraction, neural network validation, a hybrid neural network classifier and a hamming neural network lexicon. The work is based on our earlier promising results in this area using heuristic segmentation and contour based feature extraction. The segmentation is done using many heuristic based set of rules in an iterative manner and finally followed by a neural network validation system. The extraction of feature is performed using both contour and structure based feature extraction algorithm. The classification is performed by a hybrid neural network that incorporates a hybrid combination of evolutionary algorithm and matrix based solution method. Finally a hamming neural network is used as a lexicon. A benchmark dataset from CEDAR has been used for training and testing.

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