scholarly journals Development and analysis of fuzzy expert data for technological adjustment of a grain harvester header

2020 ◽  
Vol 175 ◽  
pp. 05027
Author(s):  
Valery Dimitrov ◽  
Lyudmila Borisova ◽  
Inna Nurutdinova
Keyword(s):  
Intelligent System ◽  
Expert Knowledge ◽  
Quality Criteria ◽  
Optimal Number ◽  
Membership Functions ◽  
Object Domain ◽  
Estimate Quality ◽  
Intelligent Information ◽  
The Given ◽  
Expert Data

The paper considers the problems of developing and presenting fuzzy expert data on external factors and adjustable parameters of the harvester header. The object domain “Technological adjustment of the harvester header” was studied. On the basis of the data, obtained from four experts a linguistic description of the problem statements was given, linguistic variables were introduced, membership functions were developed, consistency characteristic properties were calculated. The base of fuzzy expert knowledge intended for the unit of obtaining and updating knowledge of the decision support intelligent system by an operator in the field conditions was created. In order to estimate quality of the fuzzy expert data and define the degree of its suitability for application in intelligent information system we used the algorithm which provides setting the quality criteria, availability of feedback with experts to update the data, makes it possible to choose the optimal number of terms of the membership functions. The possibility of taking into account the expert data hierarchy in the given algorithm made it possible to introduce experts ranging according to their qualification, for this purpose Fishburn numbers were used as weightihg factors.

scholarly journals EHreact: Extended Hasse Diagrams for the Extraction and Scoring of Enzymatic Reaction Templates

2021 ◽  
Author(s):  
Esther Heid ◽  
Samuel Goldman ◽  
Karthik Sankaranarayanan ◽  
Connor W. Coley ◽  
Christoph Flamm ◽  
...  
Keyword(s):  
Expert Knowledge ◽  
Enzymatic Reaction ◽  
Software Tool ◽  
Optimal Number ◽  
Data Driven ◽  
Enzymatic Reactions ◽  
Hasse Diagrams ◽  
Large Databases ◽  
Rule Sets ◽  
The Given

Data-driven computer-aided synthesis planning utilizing organic or biocatalyzed reactions from large databases has gained increasing interest in the last decade, sparking the development of numerous tools to extract, apply and score general reaction templates. The generation of reaction rules for enzymatic reactions is especially challenging, since substrate promiscuity varies between enzymes, causing the optimal levels of rule specificity and optimal number of included atoms to differ between enzymes. This complicates an automated extraction from databases and has promoted the creation of manually curated reaction rule sets. Here we present EHreact, a purely data-driven open-source software tool to extract and score reaction rules from sets of reactions known to be catalyzed by an enzyme at appropriate levels of specificity without expert knowledge. EHreact extracts and groups reaction rules into tree-like structures, Hasse diagrams, based on common substructures in the imaginary transition structures. Each diagram can be utilized to output a single or a set of reaction rules, as well as calculate the probability of a new substrate to be processed by the given enzyme by inferring information about the reactive site of the enzyme from the known reactions and their grouping in the template tree. EHreact heuristically predicts the activity of a given enzyme on a new substrate, outperforming current approaches in accuracy and functionality.

scholarly journals EHreact: Extended Hasse Diagrams for the Extraction and Scoring of Enzymatic Reaction Templates

2021 ◽  
Author(s):  
Esther Heid ◽  
Samuel Goldman ◽  
Karthik Sankaranarayanan ◽  
Connor W. Coley ◽  
Christoph Flamm ◽  
...  
Keyword(s):  
Expert Knowledge ◽  
Enzymatic Reaction ◽  
Software Tool ◽  
Optimal Number ◽  
Data Driven ◽  
Enzymatic Reactions ◽  
Hasse Diagrams ◽  
Large Databases ◽  
Rule Sets ◽  
The Given

Data-driven computer-aided synthesis planning utilizing organic or biocatalyzed reactions from large databases has gained increasing interest in the last decade, sparking the development of numerous tools to extract, apply and score general reaction templates. The generation of reaction rules for enzymatic reactions is especially challenging, since substrate promiscuity varies between enzymes, causing the optimal levels of rule specificity and optimal number of included atoms to differ between enzymes. This complicates an automated extraction from databases and has promoted the creation of manually curated reaction rule sets. Here we present EHreact, a purely data-driven open-source software tool to extract and score reaction rules from sets of reactions known to be catalyzed by an enzyme at appropriate levels of specificity without expert knowledge. EHreact extracts and groups reaction rules into tree-like structures, Hasse diagrams, based on common substructures in the imaginary transition structures. Each diagram can be utilized to output a single or a set of reaction rules, as well as calculate the probability of a new substrate to be processed by the given enzyme by inferring information about the reactive site of the enzyme from the known reactions and their grouping in the template tree. EHreact heuristically predicts the activity of a given enzyme on a new substrate, outperforming current approaches in accuracy and functionality.

scholarly journals EHreact: Extended Hasse Diagrams for the Extraction and Scoring of Enzymatic Reaction Templates

2021 ◽  
Author(s):  
Esther Heid ◽  
Samuel Goldman ◽  
Karthik Sankaranarayanan ◽  
Connor W. Coley ◽  
Christoph Flamm ◽  
...  
Keyword(s):  
Expert Knowledge ◽  
Enzymatic Reaction ◽  
Software Tool ◽  
Optimal Number ◽  
Data Driven ◽  
Enzymatic Reactions ◽  
Hasse Diagrams ◽  
Large Databases ◽  
Rule Sets ◽  
The Given

Data-driven computer-aided synthesis planning utilizing organic or biocatalyzed reactions from large databases has gained increasing interest in the last decade, sparking the development of numerous tools to extract, apply and score general reaction templates. The generation of reaction rules for enzymatic reactions is especially challenging, since substrate promiscuity varies between enzymes, causing the optimal levels of rule specificity and optimal number of included atoms to differ between enzymes. This complicates an automated extraction from databases and has promoted the creation of manually curated reaction rule sets. Here we present EHreact, a purely data-driven open-source software tool to extract and score reaction rules from sets of reactions known to be catalyzed by an enzyme at appropriate levels of specificity without expert knowledge. EHreact extracts and groups reaction rules into tree-like structures, Hasse diagrams, based on common substructures in the imaginary transition structures. Each diagram can be utilized to output a single or a set of reaction rules, as well as calculate the probability of a new substrate to be processed by the given enzyme by inferring information about the reactive site of the enzyme from the known reactions and their grouping in the template tree. EHreact heuristically predicts the activity of a given enzyme on a new substrate, outperforming current approaches in accuracy and functionality.

The Central Role of Cognitive Computations in Human-Information Interaction

2018 ◽  
Author(s):  
Wai-Tat Fu ◽  
Jessie Chin ◽  
Q. Vera Liao
Keyword(s):  
Information Systems ◽  
Cognitive Science ◽  
Intelligent Systems ◽  
Intelligent System ◽  
Levels Of Abstraction ◽  
Intelligent Information Systems ◽  
Emergent Behaviour ◽  
Information Interaction ◽  
Intelligent Information

Cognitive science is a science of intelligent systems. This chapter proposes that cognitive science can provide useful perspectives for research on technology-mediated human-information interaction (HII) when HII is cast as emergent behaviour of a coupled intelligent system. It starts with a review of a few foundational concepts related to cognitive computations and how they can be applied to understand the nature of HII. It discusses several important properties of a coupled cognitive system and their implication to designs of information systems. Finally, it covers how levels of abstraction have been useful for cognitive science, and how these levels can inform design of intelligent information systems that are more compatible with human cognitive computations.

Finding Active Membership Functions for Genetic-Fuzzy Data Mining

2015 ◽  
Vol 14 (06) ◽  
pp. 1215-1242 ◽  
Author(s):  
Chun-Hao Chen ◽  
Tzung-Pei Hong ◽  
Yeong-Chyi Lee ◽  
Vincent S. Tseng
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Fuzzy Data ◽  
Membership Functions ◽  
Linguistic Terms ◽  
Fuzzy Association Rules ◽  
Fitness Functions ◽  
The Given ◽  
Binary Strings ◽  
Active Membership

Since transactions may contain quantitative values, many approaches have been proposed to derive membership functions for mining fuzzy association rules using genetic algorithms (GAs), a process known as genetic-fuzzy data mining. However, existing approaches assume that the number of linguistic terms is predefined. Thus, this study proposes a genetic-fuzzy mining approach for extracting an appropriate number of linguistic terms and their membership functions used in fuzzy data mining for the given items. The proposed algorithm adjusts membership functions using GAs and then uses them to fuzzify the quantitative transactions. Each individual in the population represents a possible set of membership functions for the items and is divided into two parts, control genes (CGs) and parametric genes (PGs). CGs are encoded into binary strings and used to determine whether membership functions are active. Each set of membership functions for an item is encoded as PGs with real-number schema. In addition, seven fitness functions are proposed, each of which is used to evaluate the goodness of the obtained membership functions and used as the evolutionary criteria in GA. After the GA process terminates, a better set of association rules with a suitable set of membership functions is obtained. Experiments are made to show the effectiveness of the proposed approach.

Understanding Based Managing Support Systems

2010 ◽  
pp. 91-102
Author(s):  
Lidia Ogiela ◽  
Ryszard Tadeusiewicz ◽  
Marek R. Ogiela
Keyword(s):  
Decision Making ◽  
Information Systems ◽  
Semantic Analysis ◽  
Support Systems ◽  
Expert Knowledge ◽  
Management Support ◽  
Economic Data ◽  
Economic Information ◽  
Management Support Systems ◽  
Intelligent Information

This publication presents cognitive systems designed for analysing economic data. Such systems have been created as the next step in the development of classical DSS systems (Decision Support Systems), which are currently the most widespread tools providing computer support for economic decision-making. The increasing complexity of decision-making processes in business combined with increasing demands that managers put on IT tools supporting management cause DSS systems to evolve into intelligent information systems. This publication defines a new category of systems - UBMSS (Understanding Based Management Support Systems) which conduct in-depth analyses of data using on an apparatus for linguistic and meaning-based interpretation and reasoning. This type of interpretation and reasoning is inherent in the human way of perceiving the world. This is why the authors of this publication have striven to perfect the scope and depth of computer interpretation of economic information based on human processes of cognitive data analysis. As a result, they have created UBMSS systems for the automatic analysis and interpretation of economic data. The essence of the proposed approach to the cognitive analysis of economic data is the use of the apparatus for the linguistic description of data and for semantic analysis. This type of analysis is based on expectations generated automatically by a system which collects resources of expert knowledge, taking into account the information which can significantly characterise the analysed data. In this publication, the processes of classical data description and analysis are extended to include cognitive processes as well as reasoning and forecasting mechanisms. As a result of the analyses shown, we will present a new class of UBMSS cognitive economic information systems which automatically perform a semantic analysis of business data.

scholarly journals Intelligent System for Interactive Teaching through Videogames

2020 ◽  
Vol 12 (9) ◽  
pp. 3573 ◽  
Author(s):  
Diego Robles ◽  
Christian G. Quintero M.
Keyword(s):  
High School ◽  
High School Students ◽  
Information And Communication Technologies ◽  
Intelligent Systems ◽  
Intelligent System ◽  
Computational Techniques ◽  
Case Based Reasoning ◽  
School Students ◽  
Interactive Teaching ◽  
The Given

Education, videogames, and intelligent systems are all relevant topics for researchers. Determining means of improving academic performance using a range of techniques and tools is an important challenge. However, while there are currently websites and multimedia resources that help students to improve their knowledge on specific topics, these lack in not having intelligent agents that can evaluate students and recommend materials to suit the difficulty that a user is having in a given subject. In this sense, this paper aims at developing an intelligent system that allows interactive teaching in basic education using videogames. In particular, high school students’ skills in basic mathematical operations with fractions were used for testing experimentally the approach. An intelligent system was developed using computational techniques such as fuzzy logic and case-based reasoning to evaluate user performance and recommend additional study material according to the specific challenges from the given educational game. The use of the games was supported by ICT (information and communication technologies) tools on a web platform. Such a developed platform was tested by 206 high school students, who played 5400 games in total. The students showed an improvement of around 14% in the topics covered. The results indicate that the implementation jointly of videogames and intelligent systems allows users to improve their performance in the given topics.

Tourism development in transition economies of the western Balkans

2019 ◽  
Vol 11 (1) ◽  
pp. 45-64 ◽  
Author(s):  
Tine Lehmann ◽  
Werner Gronau
Keyword(s):  
Transition Economies ◽  
Single Case ◽  
Tourism Development ◽  
Secondary Data ◽  
Quality Criteria ◽  
Southeast Europe ◽  
Western Balkans ◽  
Tourism Product ◽  
Local Actors ◽  
The Given

Abstract Tourism development in transition economies is characterized by hazards and incomplete setups. Tourists encounter lacking accommodation standard, full trash bins, insufficient health care and security infrastructure. Hence, tourism development actors in transition economies are often criticized for the incomplete touristic product. In this article we will have a more detailed look on this criticism and analyse the background of this discussion. We follow the criticism and analyse the given institutional constrains that influence tourism development in transition economies. We therefore question, what are the effects of these institutional constrains on tourism development in transition economies. We follow a qualitative approach with a single case study from a cross-border hiking trail in southeast Europe. Our data is based on secondary data, participant observer material and interviews with local actors. We demonstrate that transition economies face institutional voids, while performing well, when it comes to increasing tourism numbers. Therefore, the contribution aims on providing possible explanation on the obvious contradiction of a partly incomplete, but successful tourism product. The authors question the importance of classical quality criteria in case of the specific tourism product and the specific clientele while stressing the dimensions of authenticity, credibility and homogeneity of the given tourism setting.

GENETIC-FUZZY MINING WITH TAXONOMY

2012 ◽  
Vol 20 (supp02) ◽  
pp. 187-205 ◽  
Author(s):  
CHUN-HAO CHEN ◽  
TZUNG-PEI HONG ◽  
YEONG-CHYI LEE
Keyword(s):  
Association Rules ◽  
Multiple Level ◽  
Membership Functions ◽  
Rule Mining ◽  
Fuzzy Association Rules ◽  
Fitness Value ◽  
Real World Applications ◽  
Fuzzy Association Rule ◽  
Fuzzy Association Rule Mining ◽  
The Given

Data mining is most commonly used in attempts to induce association rules from transaction data. Since transactions in real-world applications usually consist of quantitative values, many fuzzy association-rule mining approaches have been proposed on single- or multiple-concept levels. However, the given membership functions may have a critical influence on the final mining results. In this paper, we propose a multiple-level genetic-fuzzy mining algorithm for mining membership functions and fuzzy association rules using multiple-concept levels. It first encodes the membership functions of each item class (category) into a chromosome according to the given taxonomy. The fitness value of each individual is then evaluated by the summation of large 1-itemsets of each item in different concept levels and the suitability of membership functions in the chromosome. After the GA process terminates, a better set of multiple-level fuzzy association rules can then be expected with a more suitable set of membership functions. Experimental results on a simulation dataset also show the effectiveness of the algorithm.

Affinity Propagation for the Context Quantization

2013 ◽  
Vol 791-793 ◽  
pp. 1533-1536 ◽  
Author(s):  
Min Chen ◽  
Jian Hua Chen ◽  
Mo Hai Guo
Keyword(s):  
Probability Distributions ◽  
Conditional Entropy ◽  
Optimal Number ◽  
Affinity Propagation ◽  
Code Length ◽  
Propagation Algorithm ◽  
Affinity Propagation Algorithm ◽  
Number Of Classes ◽  
The Given ◽  
Adaptive Code

In this paper, the context quantization for I-ary sources based on the affinity propagation algorithm is presented. In purpose of finding the optimal number of classes, the increment of the adaptive code length is suggested to be the similarity measure between two conditional probability distributions, by which the similarity matrix is constructed as the input of the affinity propagation algorithm. After the given number of iterations, the optimal quantizer with the optimal number of classes is achieved and the adaptive code length is minimized at the same time. The simulations indicate that the proposed algorithm produces results that are better than the results obtained by the minimum conditional entropy context quantization implemented by K-means with lower computational complexity.

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