Bio-Inspired Algorithms for Ecosystem Data Analysis

2018 ◽  
pp. 225-250
Author(s):  
Mohamed Elhadi Rahmani
Keyword(s):  
Data Analysis ◽  
Supervised Classification ◽  
Real Life ◽  
High Accuracy ◽  
Ecological Systems ◽  
External Factors ◽  
Hard Problems ◽  
Very High ◽  
Np Hard Problems

Ecological systems are known by their relationships with the environment. They affect and are affected by various external factors such as climate and the basic materials that form the soil. Good distinctions of relationships is the first important point in the modeling of ecosystems. The diversity of these systems caused a large amount of data that became hard to analyze, which made researchers classify it as NP-Hard problems. This chapter presents a study of application of bio-inspired algorithms for ecosystem data analysis. The chapter contains application of four different approaches that were inspired by authors of the paper from four different phenomena, and they were applied for analysis of four different ecosystem data collected from real life cases. Results showed a very high accuracy and proved the efficiency of bio-inspired algorithms for supervised classification of real ecosystem data.

CHANGING THE PARAMETERS OF SOCIAL REALITY UNDER THE INFLUENCE OF A GLOBAL FACTOR

2021 ◽  
Vol 74 (2) ◽  
pp. 56-60
Author(s):  
Zh.A. Kabayeva ◽  
Keyword(s):  
Data Analysis ◽  
Comparative Studies ◽  
Statistical Approach ◽  
Real Life ◽  
Social Reality ◽  
External Factors ◽  
Economic Situation ◽  
The Real ◽  
Global Factor ◽  
Public Consciousness

The aim of the article is to show how external factors influence social reality, what changes are taking place in public consciousness. One of the big factors was the COVID-19 pandemic and its attendant consequences. Complex, intricate dramatic processes are taking place that directly affect the real life of both people and communities and states. These changes, first of all, influenced the economic situation and hence, in general, the very social reality. The work uses the methods of comparative studies, phenomenology, hermeneutics. and a statistical approach to data analysis.

Supervised classification of geometrical objects by integrating currents and functional data analysis

Test ◽  
2019 ◽  
Vol 29 (3) ◽  
pp. 637-660
Author(s):  
S. Barahona ◽  
P. Centella ◽  
X. Gual-Arnau ◽  
M. V. Ibáñez ◽  
A. Simó
Keyword(s):  
Data Analysis ◽  
Functional Data Analysis ◽  
Functional Data ◽  
Supervised Classification

scholarly journals Batik Classification using Deep Learning

2019 ◽  
Vol 8 (4) ◽  
pp. 11416-11421
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Data Analysis ◽  
Convolutional Neural Network ◽  
Image Data ◽  
High Accuracy ◽  
Learning Method ◽  
Philosophy Of Life ◽  
Proposed Model

Batik is one of the Indonesian cultural heritages that has been recognized by the global community. Indonesian batik has a vast diversity in motifs that illustrate the philosophy of life, the ancestral heritage and also reflects the origin of batik itself. Because of the manybatik motifs, problems arise in determining the type of batik itself. Therefore, we need a classification method that can classify various batik motifs automatically based on the batik images. The technique of image classification that is used widely now is deep learning method. This technique has been proven of its capacity in identifying images in high accuracy. Architecture that is widely used for the image data analysis is Convolutional Neural Network (CNN) because this architecture is able to detect and recognize objects in an image. This workproposes to use the method of CNN and VGG architecture that have been modified to overcome the problems of classification of the batik motifs. Experiments of using 2.448 batik images from 5 classes of batik motifs showed that the proposed model has successfully achieved an accuracy of 96.30%.

Clustering Analysis and Algorithms

2011 ◽  
pp. 389-396
Author(s):  
Xiangji Huang
Keyword(s):  
Cluster Analysis ◽  
Data Analysis ◽  
Clustering Analysis ◽  
Supervised Classification ◽  
Unsupervised Classification ◽  
Automatic Classification ◽  
Multidimensional Space ◽  
The Difference

Clustering is the process of grouping a collection of objects (usually represented as points in a multidimensional space) into classes of similar objects. Cluster analysis is a very important tool in data analysis. It is a set of methodologies for automatic classification of a collection of patterns into clusters based on similarity. Intuitively, patterns within the same cluster are more similar to each other than patterns belonging to a different cluster. It is important to understand the difference between clustering (unsupervised classification) and supervised classification.

Clustering Analysis and Algorithms

2011 ◽  
pp. 159-164 ◽  
Author(s):  
Xiangji Huang
Keyword(s):  
Cluster Analysis ◽  
Data Analysis ◽  
Clustering Analysis ◽  
Supervised Classification ◽  
Unsupervised Classification ◽  
Automatic Classification ◽  
Multidimensional Space ◽  
The Difference

Clustering is the process of grouping a collection of objects (usually represented as points in a multidimensional space) into classes of similar objects. Cluster analysis is a very important tool in data analysis. It is a set of methodologies for automatic classification of a collection of patterns into clusters based on similarity. Intuitively, patterns within the same cluster are more similar to each other than patterns belonging to a different cluster. It is important to understand the difference between clustering (unsupervised classification) and supervised classification.

Bio-Inspired Metaheuristics

2020 ◽  
Vol 10 (4) ◽  
pp. 1-18
Author(s):  
Rachid Kaleche ◽  
Zakaria Bendaoud ◽  
Karim Bouamrane
Keyword(s):  
Real Life ◽  
Optimal Solution ◽  
Np Hard ◽  
Exact Methods ◽  
Approximate Methods ◽  
Life Problems ◽  
Comprehensive Survey ◽  
Hard Problems ◽  
Exact And Approximate Methods ◽  
Np Hard Problems

In real life, problems becoming more complicated, among them NP-Hard problems. To resolve them, two families of methods exist, exact and approximate methods. When exact methods provide the optimal solution in an unacceptable amount of time, the approximate ones provide good solutions in a reasonable amount of time. Approximate methods are two kinds, heuristics and metaheuristics. The first ones are problem specific, while metaheuristics are independent from problems. A broad number of metaheuristics are inspired from nature, specially from biology. These bio-inspired metaheuristics are easy to implement and provide interesting results. This paper aims to provide a comprehensive survey of bio-inspired metaheuristics, their classification, principals, algorithms, their application domains, and a comparison between them.

scholarly journals Supervised Classification of Very High Resolution Optical Images Using Wavelet-Based Textural Features

2016 ◽  
Vol 54 (6) ◽  
pp. 3722-3735 ◽  
Author(s):  
Olivier Regniers ◽  
Lionel Bombrun ◽  
Virginie Lafon ◽  
Christian Germain
Keyword(s):  
High Resolution ◽  
Supervised Classification ◽  
Textural Features ◽  
Optical Images ◽  
Very High
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