A Neuro-Fuzzy Model for Conflict Prediction in the Niger Delta Region of Nigeria

2021 ◽  
Vol 9 (2) ◽  
pp. 60-73
Author(s):  
Victor Ekong
Keyword(s):  
Niger Delta ◽  
Fuzzy Model ◽  
Principal Component ◽  
Original Data ◽  
Computing System ◽  
Experimental Simulation ◽  
Data Set ◽  
Conflict Risk ◽  
High Level ◽  
Conflict Prediction

This paper proposes a soft computing system driven by Neural Networks, Fuzzy Logic and Principal Component Analysis (PCA) for the prediction of conflict in the Niger Delta (ND) region of Nigeria. Identifying conflicts along the level of severity with which they arise in oil bearing host communities (OBHC) is of primary importance to government and other stakeholders, as the accompanying conflict risks mitigation course administered could be planned based on the level of severity of the conflict situations. The system is implemented using MATLAB and Microsoft Excel running on Microsoft Windows 10 operating system. The data set chosen for classification and experimental simulation is based on a statistical data obtained from a three-year field study of the nine states of the ND region of Nigeria. The average training and testing errors of 0.015514 and 0.053247 were obtained at 50 epochs for the model using a hybrid algorithm. PCA reduced the dimension of the original data set at a Cronbach’s alpha of over 0.8 with a 10-fold cross validation, thereby reducing the computational complexity and inference time of the model. The model predicted the conflict risk with an average accuracy of 92.85% and this compared favourably with domain experts conventional conflict prediction approaches. The result obtained gives a promising conclusion that the model is effective in predicting at high level of accuracy, the degree of conflict and presents a veritable decision support for conflict resolution and mediation agencies and stakeholders.

Assessment of performance using MPSS based DEA

2015 ◽  
Vol 22 (4) ◽  
pp. 624-642 ◽  
Author(s):  
Subhadip Sarkar
Keyword(s):  
Principal Component ◽  
Original Data ◽  
Data Envelopment ◽  
Data Set ◽  
Content Type ◽  
Cost Strategy ◽  
A New Technique ◽  
Most Productive Scale Size ◽  
Practical Implications

Purpose – Identification of the best school among other competitors is done using a new technique called most productive scale size based data envelopment analysis (DEA). The paper aims to discuss this issue. Design/methodology/approach – A non-central principal component analysis is used here to create a new plane according to the constant return to scale. This plane contains only ultimate performers. Findings – The new method has a complete discord with the results of CCR DEA. However, after incorporating the ultimate performers in the original data set this difference was eliminated. Practical implications – The proposed frontier provides a way to identify those DMUs which follow cost strategy proposed by Porter. Originality/value – A case study of six schools is incorporated here to identify the superior school and also to visualize gaps in their performances.

Principal Component Analyses of Potential Repository Groundwaters

1999 ◽  
Vol 556 ◽  
Author(s):  
H. Sasamoto ◽  
P. Salter ◽  
M. Apted ◽  
M. Yuiv
Keyword(s):  
Radioactive Waste ◽  
Principal Component ◽  
Chemical Components ◽  
Compacted Clay ◽  
Multivariate Statistical ◽  
Data Set ◽  
High Level Radioactive Waste ◽  
Scenario Analyses ◽  
Statistical Analysis Technique ◽  
High Level

AbstractThe chemical composition of ambient groundwater for a geological, high level radioactive waste repository is of crucial significance to issues such as radioelement solubility limits, sorption, corrosion of the overpack, behavior of compacted clay buffers, and many other factors involved in repository safety assessment. At this time, there are no candidate repository sites established in Japan for the geological disposal of high-level radioactive waste, and only generic rock formations are under consideration. It is important that a small, but representative set of groundwater types be identified so that defensible models and data for generic repository performance assessment can be established. Over 15,000 separate analyses of Japanese groundwaters have been compiled into a data set for the purpose of evaluating the range of geochemical conditions for waste repositories in Japan. This paper demonstrates the use of a multivariate statistical analysis technique, principal component analysis (PCA), to derive a set of statistically based, representative groundwater categories from the multiple chemical components and temperature that characterize the deep Japanese groundwater analyses. PCA also can be used to guide the selection of groundwaters that could be used in scenario analyses of future geological events in Japan.

scholarly journals Multivariate techniques in the analysis of carcass traits of Morada Nova breed sheep

2018 ◽  
Vol 48 (9) ◽  
Author(s):  
Déborah Galvão Peixôto Guedes ◽  
Maria Norma Ribeiro ◽  
Francisco Fernando Ramos de Carvalho
Keyword(s):  
Total Variation ◽  
Principal Components ◽  
Strong Association ◽  
Principal Component ◽  
Original Data ◽  
Discriminatory Power ◽  
Multivariate Techniques ◽  
Data Set ◽  
Canonical Variables ◽  
And Performance

ABSTRACT: This study aimed to use multivariate techniques of principal component analysis and canonical discriminant analysis in a data set from Morada Nova sheep carcass to reduce the dimensions of the original data set, identify variables with the best discriminatory power among the treatments, and quantify the association between biometric and performance traits. The principal components obtained were efficient in reducing the total variation accumulated in 19 original variables correlated to five linear combinations, which explained 80% of the total variation present in the original variables. The first two principal components together accounted for 56.12% of the total variation of the evaluated variables. Eight variables were selected using the stepwise method. The first three canonical variables were significant, explaining 92.25% of the total variation. The first canonical variable showed a canonical correlation coefficient of 0.94, indicating a strong association between biometric traits and animal performance. Slaughter weight and hind width were selected because these variables presented the highest discriminatory power among all treatments, based on standard canonical coefficients.

scholarly journals Simplified representation of atmospheric aerosol size distributions using absolute principal component analysis

2007 ◽  
Vol 7 (3) ◽  
pp. 875-886 ◽  
Author(s):  
T. W. Chan ◽  
M. Mozurkewich
Keyword(s):  
Principal Component Analysis ◽  
Size Distribution ◽  
Principal Component ◽  
Original Data ◽  
Component Analysis ◽  
New Method ◽  
Aerosol Size Distribution ◽  
Distribution Data ◽  
Complex Data ◽  
Data Set

Abstract. Principal component analysis provides a fast and robust method to reduce the data dimensionality of an aerosol size distribution data set. Here we describe a methodology for applying principal component analysis to aerosol size distribution measurements. We illustrate the method by applying it to data obtained during five field studies. Most variations in the sub-micrometer aerosol size distribution over periods of weeks can be described using 5 components. Using 6 to 8 components preserves virtually all the information in the original data. A key aspect of our approach is the introduction of a new method to weight the data; this preserves the orthogonality of the components while taking the measurement uncertainties into account. We also describe a new method for identifying the approximate number of aerosol components needed to represent the measurement quantitatively. Applying Varimax rotation to the resultant components decomposes a distribution into independent monomodal distributions. Normalizing the components provides physical meaning to the component scores. The method is relatively simple, computationally fast, and numerically robust. The resulting data simplification provides an efficient method of representing complex data sets and should greatly assist in the analysis of size distribution data.

scholarly journals Simplified representation of atmospheric aerosol size distributions using absolute principal component analysis

2006 ◽  
Vol 6 (5) ◽  
pp. 10463-10492
Author(s):  
T. W. Chan ◽  
M. Mozurkewich
Keyword(s):  
Principal Component Analysis ◽  
Size Distribution ◽  
Principal Component ◽  
Original Data ◽  
Component Analysis ◽  
New Method ◽  
Aerosol Size Distribution ◽  
Distribution Data ◽  
Complex Data ◽  
Data Set

Abstract. Principal component analysis provides a fast and robust method to reduce the data dimensionality of an aerosol size distribution data set. Here we describe a methodology for applying principal component analysis to aerosol size distribution measurements. We illustrate the method by applying it to data obtained during five field studies. Most variations in the sub-micrometer aerosol size distribution over periods of weeks can be described using 5 components. Using 6 to 8 components preserves virtually all the information in the original data. A key aspect of our approach is the introduction of a new method to weight the data; this preserves the orthogonality of the components while taking the measurement uncertainties into account. We also describe a new method for identifying the approximate number of aerosol components needed to represent the measurement quantitatively. Applying Varimax rotation to the resultant components decomposes a distribution into independent monomodal distributions. Normalizing the components provides physical meaning to the component scores. The method is relatively simply, computationally fast, and numerically robust. The resulting data simplification provides an efficient method of representing complex data sets and should greatly assist in the analysis of size distribution data.

scholarly journals Assessment of Under Five Child Mortality in Tanzania Mainland Based on Principal Component Analysis

2019 ◽  
pp. 1-13
Author(s):  
Privatus Christopher
Keyword(s):  
Principal Component Analysis ◽  
Child Mortality ◽  
Vitamin A Deficiency ◽  
Principal Component ◽  
Original Data ◽  
Component Analysis ◽  
Data Set ◽  
Under Five ◽  
Long Time ◽  
Main Components

Deaths of children younger than 5 years has been a global problem for long time. This study is focused on evaluating diseases that caused under five child mortality in Tanzania in 2013. Diseases that causes child mortality were collected from 25 regions and analysed for 42 disease variables. The data obtained were standardized and subjected to principal component analysis (PCA) to define the diseases responsible for the variability in child mortality. PCA produced seven significant main components that explain 73:40% of total variance of the original data set. The results reveal that Thyroid Diseases, Snake and Insect Bites, Vitamin A Deficiency /Xerophthalmia, Eye Infections, Schistosomiasis (SS), Intestinal Worms, Ear Infections, Haematological Diseases, Diabetes Mellitus, Ill Defined Symptoms no Diagnosis, Poisoning, Anaemia, HIV/AIDS, Burns, Rheumatic Fever, Bronchial Asthma, Peri-natal conditions and Urinary tract infection are most significant diseases in assessing under five child mortality in Tanzania mainland. This study suggest that PCA technique is useful tool for identification of important diseases that causes death of children less than five years.

scholarly journals Identification of informative performance traits in swine using principal component analysis

2005 ◽  
Vol 57 (6) ◽  
pp. 805-810 ◽  
Author(s):  
L. Barbosa ◽  
P.S. Lopes ◽  
A.J. Regazzi ◽  
S.E.F. Guimarães ◽  
R.A. Torres
Keyword(s):  
Principal Component Analysis ◽  
Litter Size ◽  
Average Daily Gain ◽  
Principal Component ◽  
Original Data ◽  
Relevant Information ◽  
Component Analysis ◽  
Data Set ◽  
Performance Traits ◽  
Eigen Values

Using principal component analysis, records of 435 animals from an F2 swine population were used to identity independent and informative variables of economically important performance. The following performance traits were recorded: litter size at birth (BL), litter size at weaning (WL), teat number (TN), birth weight (BW), weight at 21 (W21), 42 (W42), 63 (W63) and 77 (W77) days of age, average daily gain (ADG), feed intake (FI) and feed:gain ratio (FGR) from 77 to 105 days of age. Six principal components expressed variation lower than 0.7 (eigen values lower than 0.7) suggesting that six variables could be discarded with little information loss. The discarded variables present significant simple linear correlation with the retained variables. Retaining variables BL, TN, W77, FI and FGR and eliminating all the rest would retain most of the relevant information in the original data set.

Patterns of variation and correlation in quality control characteristics of segment installation

2015 ◽  
Vol 32 (3) ◽  
pp. 236-249 ◽  
Author(s):  
S. Mohammad E. Hosseininasab ◽  
Mohammad Javad Ershadi
Keyword(s):  
Quality Control ◽  
Principal Component ◽  
Original Data ◽  
Tunnel Lining ◽  
Data Set ◽  
Content Type ◽  
Applicable Knowledge ◽  
And Performance ◽  
Different Characteristics

Purpose – Evaluation of the quality and performance of a tunnel lining during the installation of segments are the main objects of tunneling projects. Because the quality is affected by several attributes, the purpose of this paper is an appropriate multivariate data analysis that is helpful in extracting applicable knowledge of the data collected regarding the related attributes of the initial installed rings. Design/methodology/approach – Principal component analysis (PCA) is used to analyze the data obtained by the quality control team. The authors use canonical correlation analysis (CCA) to extract some linear combinations of the original attributes of the two groups that produce the largest correlations with the second set of variables. Findings – The authors reduce the dimensionality of the original data set for further analyses, and use a small number of uncorrelated variables rather than a larger set of correlated variables to take effective and efficient action to control the quality of the tunnel lining. The authors also explore the correlation structure and relationship between two main groups of characteristics used for assessing the quality of the installed rings. Then, instead of a large number of the original characteristics in the two groups, the authors can easily control these few to attain a reasonable quality for the tunnel lining. Originality/value – This is a case study, and for each ring selected for inspection, 16 different characteristics are measured and the observations are recorded. The authors use PCA and CCA to analyse the data and interpret the results. Although the methods are not new, applying them to this data results in useful and informative outcomes and interpretation.

scholarly journals Optimal distribution-preserving downsampling of large biomedical data sets (opdisDownsampling)

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0255838
Author(s):  
Jörn Lötsch ◽  
Sebastian Malkusch ◽  
Alfred Ultsch
Keyword(s):  
Standard Method ◽  
Selection Criterion ◽  
Principal Component ◽  
Original Data ◽  
Data Sets ◽  
Biomedical Data ◽  
Optimal Distribution ◽  
Current Standard ◽  
Data Set ◽  
Entire Data

Motivation The size of today’s biomedical data sets pushes computer equipment to its limits, even for seemingly standard analysis tasks such as data projection or clustering. Reducing large biomedical data by downsampling is therefore a common early step in data processing, often performed as random uniform class-proportional downsampling. In this report, we hypothesized that this can be optimized to obtain samples that better reflect the entire data set than those obtained using the current standard method. Results By repeating the random sampling and comparing the distribution of the drawn sample with the distribution of the original data, it was possible to establish a method for obtaining subsets of data that better reflect the entire data set than taking only the first randomly selected subsample, as is the current standard. Experiments on artificial and real biomedical data sets showed that the reconstruction of the remaining data from the original data set from the downsampled data improved significantly. This was observed with both principal component analysis and autoencoding neural networks. The fidelity was dependent on both the number of cases drawn from the original and the number of samples drawn. Conclusions Optimal distribution-preserving class-proportional downsampling yields data subsets that reflect the structure of the entire data better than those obtained with the standard method. By using distributional similarity as the only selection criterion, the proposed method does not in any way affect the results of a later planned analysis.

scholarly journals A New Privacy Preserving Association Rule Mining Algorithm Based on Hybrid Partial Hiding Strategy

2013 ◽  
Vol 13 (Special-Issue) ◽  
pp. 41-50 ◽  
Author(s):  
Jian-Ming Zhu ◽  
Ning Zhang ◽  
Zhan-Yu Li
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Privacy Preservation ◽  
Original Data ◽  
Rule Mining ◽  
Data Set ◽  
Original Algorithm ◽  
Level Data ◽  
Frequent Items ◽  
High Level

Abstract Data mining is the progress of automatically discovering high level data and trends in large amounts of data that would otherwise remain hidden. In order to improve the privacy preservation of association rule mining, a hybrid partial hiding algorithm (HPH) is proposed. The original data set can be interfered and transformed by different random parameters. Then, the algorithm of generating frequent items based on HPH is presented. Finally, it can be proved that the privacy of HPH algorithm is better than that of the original algorithm.

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