scholarly journals Integrating DROOLS and R software for intelligent map system

2011 ◽  
Vol 7 ◽  
pp. 85-92
Author(s):  
Jan Růžička
Keyword(s):  
Expert System ◽  
Statistical Tests ◽  
Data Classification ◽  
R Software ◽  
Statistical Software ◽  
Selection Of

The paper describes intelligent map system that allows to check errors in map sheets or to help with a map sheet creation. The system is based on expert system DROOLS, ontology created in Protége and statistical software R. Prototype of the system should evaluate that this kind of integration is possible, so the system is not full of rules. The prototype is filled with twenty rules written in DRL language and with more than thirty items from the ontology. The paper should show how all of these components can be integrated together to allow such kind of a map sheet evaluation. The system is now used for selection of the best method for data classification. The selection is suggested by DROOLS system that uses R software to perform statistical tests of normality and uniformity.

An expert system for the selection of strategic planning technique

Kybernetes ◽  
2002 ◽  
Vol 31 (3/4) ◽  
pp. 550-560 ◽  
Author(s):  
Suresh Subramoniam ◽  
K.V. Krishnankutty
Keyword(s):  
Expert System ◽  
Strategic Planning ◽  
Planning Technique ◽  
Selection Of

Preventive maintenance scheduling using analysis of variance-based ant lion optimizer

2018 ◽  
Vol 15 (2) ◽  
pp. 254-272 ◽  
Author(s):  
Umamaheswari Elango ◽  
Ganesan Sivarajan ◽  
Abirami Manoharan ◽  
Subramanian Srikrishna
Keyword(s):  
Analysis Of Variance ◽  
Heuristic Algorithm ◽  
Heuristic Algorithms ◽  
Statistical Tests ◽  
Population Based ◽  
Maintenance Scheduling ◽  
Content Type ◽  
Ant Lion Optimizer ◽  
Ant Lion ◽  
Selection Of

Purpose Generator maintenance scheduling (GMS) is an essential task for electric power utilities as the periodical maintenance activity enhances the lifetime and also ensures the reliable and continuous operation of generating units. Though numerous meta-heuristic algorithms have been reported for the GMS solution, enhancing the existing techniques or developing new optimization procedure is still an interesting research task. The meta-heuristic algorithms are population based and the selection of their algorithmic parameters influences the quality of the solution. This paper aims to propose statistical tests guided meta-heuristic algorithm for solving the GMS problems. Design/methodology/approach The intricacy characteristics of the GMS problem in power systems necessitate an efficient and robust optimization tool. Though several meta-heuristic algorithms have been applied to solve the chosen power system operational problem, tuning of their control parameters is a protracting process. To prevail over the previously mentioned drawback, the modern meta-heuristic algorithm, namely, ant lion optimizer (ALO), is chosen as the optimization tool for solving the GMS problem. Findings The meta-heuristic algorithms are population based and require proper selection of algorithmic parameters. In this work, the ANOVA (analysis of variance) tool is proposed for selecting the most feasible decisive parameters in algorithm domain, and the statistical tests-based validation of solution quality is described. The parametric and non-parametric statistical tests are also performed to validate the selection of ALO against the various competing algorithms. The numerical and statistical results confirm that ALO is a promising tool for solving the GMS problems. Originality/value As a first attempt, ALO is applied to solve the GMS problem. Moreover, the ANOVA-based parameter selection is proposed and the statistical tests such as Wilcoxon signed rank and one-way ANOVA are conducted to validate the applicability of the intended optimization tool. The contribution of the paper can be summarized in two folds: the ANOVA-based ALO for GMS applications and statistical tests-based performance evaluation of intended algorithm.

Practical biomedical statistics: A guide to the selection of statistical tests

Urology ◽  
1996 ◽  
Vol 47 (1) ◽  
pp. 2-13 ◽  
Author(s):  
J. Stuart Wolf ◽  
Deborah S. Smith
Keyword(s):  
Statistical Tests ◽  
Biomedical Statistics ◽  
Selection Of

SISTEM PAKAR MENENTUKAN PENYAKIT GINJAL DENGAN METODE FORWARD CHAINING

2020 ◽  
Vol 11 (1) ◽  
pp. 27-32
Author(s):  
Wahyu Alfandry Pulungan
Keyword(s):  
Expert System ◽  
Kidney Disease ◽  
Disease Diagnosis ◽  
Metabolic System ◽  
Consultation Process ◽  
The Public ◽  
System Disease ◽  
Visual Basic 6.0 ◽  
High Calorie ◽  
Selection Of

Selection of issues regarding the kind of kidney disease as a sample of this study, is the fact that diseases Kidney is an important organ in our body's metabolic system, because the density of activity, we often forget to take care of. Irregular diet, inadequate intake of fiber and mineral water, as well as the consumption of food or drink high calorie instant, unwittingly aggravate the kidneys. Starting from the filtration, reabsorption, to augmentation of nutrients that under to the kidneys via the blood. The purpose of this research is to build an expert system Kidney disease using Visual Basic 6.0 programming language that is capable of providing services to the public and delivery of information related to kidney disease. In this research, data collection is done by using the method of observation, interviews, and literature. From the results of this study indicate that the presence of kidney disease diagnosis expert system in humans can provide significant benefits, among others, the processing of data and consultation process carried out quickly and produce a fairly accurate report, thus making the job more effectively and efficiently. Keywords: Expert System, Disease, Kidney, Human.

Practical R for biologists: an introduction

2021 ◽  
Keyword(s):  
Statistical Tests ◽  
Statistical Modelling ◽  
Biological Data ◽  
Early Years ◽  
Main Text ◽  
Biological Data Analysis ◽  
Base Functions ◽  
Almost All ◽  
Selection Of ◽  
R Functions

Abstract R is an open-source statistical environment modelled after the previously widely used commercial programs S and S-Plus, but in addition to powerful statistical analysis tools, it also provides powerful graphics outputs. In addition to its statistical and graphical capabilities, R is a programming language suitable for medium-sized projects. This book presents a set of studies that collectively represent almost all the R operations that beginners, analysing their own data up to perhaps the early years of doing a PhD, need. Although the chapters are organized around topics such as graphing, classical statistical tests, statistical modelling, mapping and text parsing, examples have been chosen based largely on real scientific studies at the appropriate level and within each the use of more R functions is nearly always covered than are simply necessary just to get a p-value or a graph. R comes with around a thousand base functions which are automatically installed when R is downloaded. This book covers the use of those of most relevance to biological data analysis, modelling and graphics. Throughout each chapter, the functions introduced and used in that chapter are summarized in Tool Boxes. The book also shows the user how to adapt and write their own code and functions. A selection of base functions relevant to graphics that are not necessarily covered in the main text are described in Appendix 1, and additional housekeeping functions in Appendix 2.

scholarly journals P nucleotides in V(D)J recombination: a fine-structure analysis.

1993 ◽  
Vol 13 (2) ◽  
pp. 1078-1092 ◽  
Author(s):  
J T Meier ◽  
S M Lewis
Keyword(s):  
Fine Structure ◽  
Germ Line ◽  
Statistical Tests ◽  
Random Sequence ◽  
Genetic Locus ◽  
Gene Segment ◽  
Lymphoid Cells ◽  
Base Pairs ◽  
Nucleotide Data ◽  
Selection Of

Antigen receptor genes acquire junctional inserts upon assembly from their component, germ line-encoded V, D, and J segments. Inserts are generally of random sequence, but a small number of V-D, D-J, or V-J junctions are exceptional. In such junctions, one or two added base pairs inversely repeat the sequence of the abutting germ line DNA. (For example, a gene segment ending AG might acquire an insert beginning with the residues CT upon joining). It has been proposed that the nonrandom residues, termed "P nucleotides," are a consequence of an obligatory end-modification step in V(D)J recombination. P insertion in normal, unselected V(D)J joining products, however, has not been rigorously established. Here, we use an experimentally manipulable system, isolated from immune selection of any kind, to examine the fine structure of V(D)J junctions formed in wild-type lymphoid cells. Our results, according to statistical tests, show the following, (i) The frequency of P insertion is influenced by the DNA sequence of the joined ends. (ii) P inserts may be longer than two residues in length. (iii) P inserts are associated with coding ends only. Additionally, a systematic survey of published P nucleotide data shows no evidence for variation in P insertion as a function of genetic locus and ontogeny. Together, these analyses establish the generality of the P nucleotide pattern within inserts but do not fully support previous conjectures as to their origin and centrality in the joining reaction.

scholarly journals Data Size Requirement for Forecasting Daily Crude Oil Price with Neural Networks

2019 ◽  
Vol 66 (3) ◽  
pp. 363-388
Author(s):  
Serkan Aras ◽  
Manel Hamdi
Keyword(s):  
Neural Networks ◽  
Crude Oil ◽  
Significant Interaction ◽  
Statistical Tests ◽  
Oil Price ◽  
Training Data ◽  
Forecasting Model ◽  
Crude Oil Price ◽  
Crude Oil Prices ◽  
Selection Of

When the literature regarding applications of neural networks is investigated, it appears that a substantial issue is what size the training data should be when modelling a time series through neural networks. The aim of this paper is to determine the size of training data to be used to construct a forecasting model via a multiple-breakpoint test and compare its performance with two general methods, namely, using all available data and using just two years of data. Furthermore, the importance of the selection of the final neural network model is investigated in detail. The results obtained from daily crude oil prices indicate that the data from the last structural change lead to simpler architectures of neural networks and have an advantage in reaching more accurate forecasts in terms of MAE value. In addition, the statistical tests show that there is a statistically significant interaction between data size and stopping rule.

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