Influence of information noise on inference procedures of fuzzy expert systems for fermentation control and management

A possibility for enrichment of the methodology of expert system development based on a simulation of the probabilistic component of information vagueness is proposed. Using this concept, Gaussian noise with different sizes of dispersion was applied to the experimental values of the main physiological state variables in phenylalanine fermentation by genetically manipulated Echerichia coli. These variables were introduced at the input of the recognition block of the expert system, imitating noisy experimental data. It is shown that implementation of this approach can reveal some important characteristics of expert systems and can be useful for their improvement.This study was carried out within the framework of Bulgaria-UNDP Joint Project DP/BUL/007/86.The authors express their gratitude to Mr. Ricaredo Matanguihan for his assistance during the numerical examination of the fuzzy expert system for the control and management of phenylalanine fermentation.     

OURCIN: a tool to build expert systems

OURCIN is a tool to build expert systems, which was developed by a joint team from INRIA and SEMA. We emphasized developing on this system the ergonomic features which make Expert Systems a comfortable and interesting approach for knowledge engineering.     

集成的专家系统和神经网络应用于大熊猫生境评价

充分了解大熊猫生境的时空格局及其变化,对有效保护大熊猫非常重要.绘制生境图既是野生动物生境评价和监测的一个有效方式,也是一个必要的步骤.新发展起来的人工智能方法(包括专家系统和神经网络方法),在模拟复杂系统过程中能够同时综合定性和定量信息,并可集成于GIS中,有助于大熊猫复杂生境的制图及评价.为了对大熊猫生境进行评价,本文建立了一个较全面的综合制图方法,将专家系统、神经网络和多类型数据全部集成在GIS环境下.结果表明,采用专家系统和神经网络集成方法绘制的大熊猫生境图的精度达到80％以上,高于单一的专家系统方法、神经网络方法和传统的最大似然法制图的精度.Z统计方法也证实了新建立的专家系统和神经网络集成方法要显著好于3种单一方法.     

An integrated approach for environmental assessments

LCA is a system-wide assessment, and the LCIA phase is confronted with the difficulties of local and regional effects in a number of impact categories. We integrate three different environmental techniques to demonstrate how these effects can be addressed in an environmental assessment. The techniques are life cycle inventory, environmental fate models, and an ecological impact assessment using fuzzy expert systems. Results of the LCI are mass and energy flows. In the environmental fate modelling step these mass flows are transformed into concentration and immission values by dispersion-reaction models. A generalised fuzzy expert system for the environmental mechanisms compares calculated exposure with site specific buffering capacities and formulates a generalised dose-response relationship. This generalised fuzzy expert system is used as a template for the assessment of local and regional environmental impacts. An application of this integrated approach is shown for a practical problem: production of magnesium car components. The environmental fate of nitrogen oxides which are released due to the major combustion source within that production system is simulated. Fuzzy expert models for crop damage, soil acidification and eutrophication determine the possible environmental impact of the immited nitrogen oxides. The important methodological extension of this integrated approach is a regionalised impact assessment depending on the spatial distribution of environmental characteristics.     

Fuzzy rule‐based decision support system for evaluation of long‐established forest restoration projects

Although the importance of monitoring and evaluation of restoration actions is increasingly acknowledged, availability of accurate, quantitative monitoring data is very rare for most restoration areas, particularly for long‐established restoration projects. We propose using fuzzy rule‐based expert systems to evaluate the degree of success of restoration actions when available information on project results and impacts largely relies on expert‐based qualitative assessments and rough estimates of quantitative values. These systems use fuzzy logic to manage the uncertainty present in the data and to integrate qualitative and quantitative information. To illustrate and demonstrate the potential of fuzzy rule‐based systems for restoration evaluation, we applied this approach to seven forest restoration projects implemented in Spain between 1897 and 1952, using information compiled in the REACTION database on Mediterranean forest restoration projects. The information available includes both quantitative and expert‐based qualitative data, and covers a wide variety of indicators grouped into technical, structural, functional, and socioeconomic criteria. The fuzzy rule‐based system translates expert knowledge of restoration specialists and forest managers into a set of simple logic rules that integrate information on individual indicators into more general evaluation criteria. The rule‐based approach proposed here can be readily applicable to any kind of restoration project, provided that some information, even if vague and uncertain, is available for a variety of assessment indicators. The evaluation of long‐established forest restoration projects implemented in Spain revealed important asymmetries in the degree of restoration success between technical, structural, functional, and socioeconomic criteria.     

Medical diagnosis: Are expert systems needed?

An evaluation of the pitfalls of computer-aided diagnosis during the last ten yearns seems to show that increased emphasis on real clinical problems as well as increased use of medical knowledge are required. Since such an approach could fit into the ‘expert system’ framework, one should expect new developments in that research field to occur soon. However, expert systems can also be designed along the same concepts as previous models of diagnostic reasoning presumably with the same shortcomings.     

BibGlimpse: The case for a light-weight reprint manager in distributed literature research

Background  

While text-mining and distributed annotation systems both aim at capturing knowledge and presenting it in a standardized form, there have been few attempts to investigate potential synergies between these two fields. For instance, distributed annotation would be very well suited for providing topic focussed, expert knowledge enriched text corpora. A key limitation for this approach is the availability of literature annotation systems that can be routinely used by groups of collaborating researchers on a day to day basis, not distracting from the main focus of their work.     

Knowledge-based expert systems and a proof-of-concept case study for multiple sequence alignment construction and analysis

The traditional approach to bioinformatics analyses relies onindependent task-specific services and applications, using differentinput and output formats, often idiosyncratic, and frequentlynot designed to inter-operate. In general, such analyses wereperformed by experts who manually verified the results obtainedat each step in the process. Today, the amount of bioinformaticsinformation continuously being produced means that handlingthe various applications used to study this information presentsa major data management and analysis challenge to researchers.It is now impossible to manually analyse all this informationand new approaches are needed that are capable of processingthe large-scale heterogeneous data in order to extract the pertinentinformation. We review the recent use of integrated expert systemsaimed at providing more efficient knowledge extraction for bioinformaticsresearch. A general methodology for building knowledge-basedexpert systems is described, focusing on the unstructured informationmanagement architecture, UIMA, which provides facilities forboth data and process management. A case study involving a multiplealignment expert system prototype called AlexSys is also presented.      

Expert systems in histopathology. I. Introduction and overview

An introduction to, and overview of, expert systems is presented, along with some preliminary comments on their application in diagnostic and analytical histopathology and cytopathology. The terminology common to expert systems is defined, and the nature of expert systems is discussed. In particular, the differences between expert systems and other types of computer programs (e.g., algorithms) or means of solving problems are explored. The rationale for their use and the types of tasks for which they are appropriate are also discussed.     

Knowledge-based control of fermentation processes

A decade has passed since the first applications of a knowledge-based approach to the control of bioprocesses were reported. During this period, both the development and application of intelligent control in biotechnology have undergone remarkable evolution in terms of concepts, objectives, and tools. Stimulated by rapid progress in the field of real-time expert systems, knowledge-based methodology for the control of fermentation processes has now reached a more mature phase. A growing interest among the biotechnology community and intensive, realistic, and fruitful research being undertaken both in universities and in industry suggest that large-scale application of knowledge-based systems for the control of bioprocesses is inevitable. This article provides a concise summary of the main achievements in this new area and discusses recent trends, porblems, and perspectives.     

Role of regulatory agencies

In this paper the authors discuss the role of regulation in assuring blood safety. After an overview of the subject by a leading expert, examples are provided of regulatory systems for blood transfusion services in several countries and regions. Additionally, the perspective of WHO is given on the essential role of national regulatory authorities in assuring the quality of national blood programmes.Collectively, the sections of this paper afford an opportunity for readers to make comparisons among different regulatory frameworks and to "benchmark" among the existing systems. Despite many differences in approach, a clear pattern emerges of worldwide efforts to strengthen blood regulatory systems.     

Counting Cats: The integration of expert and citizen science data for unbiased inference of population abundance

Free‐roaming animal populations are hard to count, and professional experts are a limited resource. There is vast untapped potential in the data collected by nonprofessional scientists who volunteer their time to population monitoring, but citizen science (CS) raises concerns around data quality and biases. A particular concern in abundance modeling is the presence of false positives that can occur due to misidentification of nontarget species. Here, we introduce Integrated Abundance Models (IAMs) that integrate citizen and expert data to allow robust inference of population abundance meanwhile accounting for biases caused by misidentification. We used simulation experiments to confirm that IAMs successfully remove the inflation of abundance estimates caused by false‐positive detections and can provide accurate estimates of both bias and abundance. We illustrate the approach with a case study on unowned domestic cats, which are commonly confused with owned, and infer their abundance by analyzing a combination of CS data and expert data. Our case study finds that relying on CS data alone, either through simple summation or via traditional modeling approaches, can vastly inflate abundance estimates. IAMs provide an adaptable framework, increasing the opportunity for further development of the approach, tailoring to specific systems and robust use of CS data.     

Risk Communications: Around the World Neural Network Models for Assessing Road Suitability for Dangerous Goods Transport

This article describes a methodology for assessing the degree of remedial action required to make short stretches of a roadway suitable for dangerous goods transport (DGT). The methodology is based on the evaluation of a set of variables that have a bearing on DGT risk. The large number of variables involved made it necessary to apply a supervised approach based on expert criteria. The result was a knowledge base that can be used both to estimate DGT risk for new stretches of roadway and to determine sources of risk without having to rely on an expert. A number of multivariate statistical analysis techniques were tested for the construction of the model, namely linear discriminant analysis with a prior reduction in dimensionality, multilayer perceptrons, and support vector machines. The results obtained from a test sample show that the support vector machines represented expert knowledge most reliably. A graphic representation of the risk index for a studied stretch of roadway results in a map of the level of DGT risk for that roadway.     

Expertise in expert systems: knowledge acquisition for biological expert systems

In this paper it is argued that an expert system requires morethan factual knowledge before it can display expertise in agiven domain. The additional knowledge consists of the heuristicsor ‘rules of thumb’ used by an expert to manipulateand interpret the factual knowledge. The knowledge acquisitionphase of an expert system project involves determining the factualknowledge (which may be obtained from published sources) andthe heuristics used by an expert to manipulate that knowledge-theseheuristics can only be obtained from an expert. In reviewingexisting biological expert systems it is apparent that manycontain only the factual knowledge relating to the domain, andlack the heuristics that enable such systems to show expertise.This paper reviews a number of knowledge acquisition techniqueswhich could be used for acquiring heuristic knowledge and discusseswhen their use is appropriate. The knowledge acquisition techniquesdiscussed are those suitable for the development of small-scaleexpert systems as these are most likely to be of interest tobiologists. The techniques include the use of questionnaires,interview techniques and protocol analysis; particular emphasisis placed on a mod cation to the ‘twenty questions’interview technique which was developed specifically to elicittaxonomic knowledge relating to water mite identification.     

Expert systems in medicine

The emergence of the artificial intelligence (AI) in computer technology and its application in the medical field enables the researchers to carry out such intelligent activities like image processing, medical reasoning systems, clinical decision supporting and natural language understanding, etc. A gastroenterological expert system application is briefly demonstrated in this paper. Similar expert systems can be seen to be useful in the research of gastrointestinal cytoprotection, including the plan of different compounds with cytoprotective effect, experimental and clinical medical research.     

Development of an expert system for preoperative assessment of female urinary incontinence

We developed a simple expert system for preoperative assessment of women complaining of involuntary loss of urine and scheduled to undergo surgery for incontinence. The aim of the system was to use the parameters obtained at urodynamic investigation to arrive at the correct diagnosis. We used an IBM-PC with two disk drives and 256k RAM, and the expert system shell EXSYS, a rule based system with the possibility of assigning probabilities to the different solutions. To write the rules forming the knowledge base we used a two-fold approach: we constructed tree diagrams for each diagnosis and calculated the corresponding predictive values (statistical approach), and we added rules based on our experience (heuristic approach). The expert system has been found reliable in a clinical setting and is useful for teaching purposes.     

Indicators of environmentally sound land use in the humid tropics: The potential roles of expert opinion,knowledge engineering and knowledge discovery

Despite abundant literature on indicators for sustainable resource management, practical tools to help local users to apply its general concepts at a local to regional level are scarce. This means that decisions over land evaluation and land use at a local level are often not based on the formal application of indicators or decision support systems for environmentally sound management but instead on the opinion of local expertise, for instance forest managers, cattle breeders, farmers and/or academics. This is particularly seen to be the case in the tropics where access to modern communication and information technologies is restricted.As the opinions of experts are often based on and influenced by personal experience, intuition, heuristics and bias, their evaluations and decision are often unclear to the non-expert working at a local level. In order to make their reasoning more comprehensible to the non-expert, the ecological condition of 176 plots in the tropical Southeast of Mexico were evaluated by experts on soil fertility, forest management, cattle breeding and agriculture. With the assistance of a knowledge engineer (one who converts expert knowledge and reasoning into a model), these expert opinions and reasoning were then translated into a formal computer model.As an alternative approach we applied a knowledge discovery technique, namely the induction of regression trees and automatically developed models using the expert evaluations as training data. Where knowledge engineering was tedious and time consuming, regression models could be rapidly generated. Moreover, the correspondence between regression trees and expert opinions was considerably higher than the correspondence between expert opinion and their own models. The regression trees used less explicative variables than the models generated by the experts. The minimisation of sampling effort due to variable space reduction means that the application of regression tree induction has a high potential for a rapid development of indicators for narrowly defined ecological assessments, needed for decision making on a local or regional scale.     

Evaluation of Epidemic Intelligence Systems Integrated in the Early Alerting and Reporting Project for the Detection of A/H5N1 Influenza Events

The objective of Web-based expert epidemic intelligence systems is to detect health threats. The Global Health Security Initiative (GHSI) Early Alerting and Reporting (EAR) project was launched to assess the feasibility and opportunity for pooling epidemic intelligence data from seven expert systems. EAR participants completed a qualitative survey to document epidemic intelligence strategies and to assess perceptions regarding the systems performance. Timeliness and sensitivity were rated highly illustrating the value of the systems for epidemic intelligence. Weaknesses identified included representativeness, completeness and flexibility. These findings were corroborated by the quantitative analysis performed on signals potentially related to influenza A/H5N1 events occurring in March 2010. For the six systems for which this information was available, the detection rate ranged from 31% to 38%, and increased to 72% when considering the virtual combined system. The effective positive predictive values ranged from 3% to 24% and F1-scores ranged from 6% to 27%. System sensitivity ranged from 38% to 72%. An average difference of 23% was observed between the sensitivities calculated for human cases and epizootics, underlining the difficulties in developing an efficient algorithm for a single pathology. However, the sensitivity increased to 93% when the virtual combined system was considered, clearly illustrating complementarities between individual systems. The average delay between the detection of A/H5N1 events by the systems and their official reporting by WHO or OIE was 10.2 days (95% CI: 6.7–13.8). This work illustrates the diversity in implemented epidemic intelligence activities, differences in system''s designs, and the potential added values and opportunities for synergy between systems, between users and between systems and users.     

Machine learning in quantitative histopathology

The role of expert systems functioning as process controllers in learning image understanding systems is discussed. Numeric learning systems already have found a number of applications in cytologic and histopathologic diagnosis. Depending on the required capabilities, systems of increasing complexity are needed. Expert systems to guide scene segmentation in histopathologic imagery require model-based reasoning. Diagnostic image interpretation with learning capability demands a full model of the human expert's competence, including a considerable variety of knowledge representation schemes and inference strategies, coordinated by a meta-process controller.