Efficiency Analysis of Genetic Algorithm and Genetic Programming in Data Mining and Image Processing

2017 ◽  
pp. 334-360
Author(s):  
Ayan Chatterjee ◽  
Mahendra Rong
Keyword(s):  
Data Mining ◽  
Image Processing ◽  
Mosaic Generation ◽  
Processor Unit ◽  
Photo Mosaic ◽  
Segmentation Of Images ◽  
Graphics Processor Unit ◽  
Image Pattern ◽  
Learning Data

Today, in the age of artificial intelligence and machine learning, Data mining and Image processing are two important platforms. GA and GP are value based and program based randomized searching tools respectively and these two are very much useful in the fields' data mining and image processing for handling different issues. In this chapter, a review is made on ability of GA and GP in some applications of these two fields. Here, the selected subfields of data mining are market analysis, fraud detection, risk management, sports analysis, protein interaction, classification of data, drug discovery and feature construction. The similar in image processing are enhancement and segmentation of images, face recognition, photo mosaic generation, data embedding, image pattern classification, object detection and Graphics Processor Unit (GPU) development. The efficiencies of GA and GP in these particular applications are analyzed with corresponding parameters, comparing with other non-GA and non-GP approaches of the corresponding subfields.

Efficiency Analysis of Genetic Algorithm and Genetic Programming in Data Mining and Image Processing

2018 ◽  
pp. 246-272
Author(s):  
Ayan Chatterjee ◽  
Mahendra Rong
Keyword(s):  
Data Mining ◽  
Image Processing ◽  
Mosaic Generation ◽  
Processor Unit ◽  
Photo Mosaic ◽  
Segmentation Of Images ◽  
Graphics Processor Unit ◽  
Image Pattern ◽  
Learning Data

Today, in the age of artificial intelligence and machine learning, Data mining and Image processing are two important platforms. GA and GP are value based and program based randomized searching tools respectively and these two are very much useful in the fields' data mining and image processing for handling different issues. In this chapter, a review is made on ability of GA and GP in some applications of these two fields. Here, the selected subfields of data mining are market analysis, fraud detection, risk management, sports analysis, protein interaction, classification of data, drug discovery and feature construction. The similar in image processing are enhancement and segmentation of images, face recognition, photo mosaic generation, data embedding, image pattern classification, object detection and Graphics Processor Unit (GPU) development. The efficiencies of GA and GP in these particular applications are analyzed with corresponding parameters, comparing with other non-GA and non-GP approaches of the corresponding subfields.

scholarly journals Techniques for Image Segmentation: A Critical Review

2021 ◽  
Vol 9 (3) ◽  
pp. 1-4
Author(s):  
Harshita Mishra ◽  
Anuradha Misra
Keyword(s):  
Data Mining ◽  
Image Processing ◽  
Image Segmentation ◽  
Digital Image ◽  
Electronic Devices ◽  
Data Mining Technique ◽  
Mining Technique ◽  
Mathematical Algorithms ◽  
Picture Element

In today’s world there is requirement of some techniques or methods that will be helpful for retrieval of the information from the images. Information those are important for finding solution to the problems in the present time are needed. In this review we will study the processing involved in the digitalization of the image. The set or proper array of the pixels that is also called as picture element is known as image. The positioning of these pixels is in matrix which is formed in columns and rows. The image undergoes the process of digitalization by which a digital image is formed. This process of digitalization is called digital image processing of the image (D.I.P). Electronic devices as such computers are used for the processing of the image into digital image. There are various techniques that are used for image segmentation process. In this review we will also try to understand the involvement of data mining for the extraction of the information from the image. The process of the identifying patterns in the large stored data with the help of statistic and mathematical algorithms is data mining. The pixel wise classification of the image segmentation uses data mining technique.

scholarly journals PERANCANGAN SISTEM KLASIFIKASI UDANG BERACUN PADA JENIS UDANG TENGGEK MENGGUNAKAN METODE K-NEAREST NEIGHBOR (K-NN).

2020 ◽  
Vol 6 (1) ◽  
pp. 1-7
Author(s):  
Lisa Afrinanda ◽  
Ilyas Ilyas
Keyword(s):  
Data Mining ◽  
Image Processing ◽  
Nearest Neighbor ◽  
The Body ◽  
K Nearest Neighbor ◽  
Class A ◽  
The People ◽  
Average Accuracy ◽  
Class B

Shrimp is one of the seafood which is nutrient-rich needed by the body. However, due to the frequent case of the infected Tenggek-shrimp appeared, it makes people beware to consume it. The classification of Tenggek-shrimp by using image processing of the computer be able to classify the types of shrimp whether poisonous or not. The data mining techniques can be used to classify shrimp based on RGB colors (red, green, blue) and texture (energy, contrast, correlation, homogeneity). The class of Tenggek-shrimp is divided into two, The fresh Tenggek-shrimps that are caught naturally (Class A) and the poisoned Tenggek-shrimps that are caught by using the poison (Class B). The method used in this study is K-Nearest Neighbor (K-NN). This classification system is expected to help the people in selecting good and safe Tenggek-shrimp for consumption. Based on the evaluation results using the holdout method, obtained an average accuracy of 63% with an accuracy of identification of toxic tenggek shrimp of 71.66%, and the accuracy of identification of natural fresh shrimp is about 60%.

High-precision Classification of Small Insects with Similar Color and Shape Using Image Processing

2016 ◽  
Vol 136 (8) ◽  
pp. 1120-1127 ◽  
Author(s):  
Naoya Ikemoto ◽  
Kenji Terada ◽  
Yuta Takashina ◽  
Akio Nakano
Keyword(s):  
Image Processing ◽  
High Precision

An Enhanced Technique for Classification of Fruits using Shape Color and Texture Features

2017 ◽  
Vol 7 (7) ◽  
pp. 408
Author(s):  
Yashpal Jitarwal ◽  
Tabrej Ahamad Khan ◽  
Pawan Mangal
Keyword(s):  
Image Processing ◽  
Texture Features ◽  
Automatic Classification ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Advance Technique ◽  
Time Required ◽  
Fruit Sorting ◽  
Human Inspection

In earlier times fruits were sorted manually and it was very time consuming and laborious task. Human sorted the fruits of the basis of shape, size and color. Time taken by human to sort the fruits is very large therefore to reduce the time and to increase the accuracy, an automatic classification of fruits comes into existence.To improve this human inspection and reduce time required for fruit sorting an advance technique is developed that accepts information about fruits from their images, and is called as Image Processing Technique.

Color Region Classification of Flue-cured Tobacco Leaves Based on the Image Processing and Support Vector Machine

2013 ◽  
Vol 38 (2) ◽  
pp. 374-379 ◽  
Author(s):  
Zhi-Li PAN ◽  
Meng QI ◽  
Chun-Yang WEI ◽  
Feng LI ◽  
Shi-Xiang ZHANG ◽  
...  
Keyword(s):  
Image Processing ◽  
Support Vector Machine ◽  
Support Vector ◽  
Tobacco Leaves ◽  
Region Classification

scholarly journals OFCOD: On the Fly Clustering Based Outlier Detection Framework

Data ◽  
2020 ◽  
Vol 6 (1) ◽  
pp. 1
Author(s):  
Ahmed Elmogy ◽  
Hamada Rizk ◽  
Amany M. Sarhan
Keyword(s):  
Data Mining ◽  
Image Processing ◽  
Intrusion Detection ◽  
Real Time ◽  
Outlier Detection ◽  
Real World ◽  
Medical Data ◽  
Experimental Results ◽  
Real Time Applications ◽  
Real World Datasets

In data mining, outlier detection is a major challenge as it has an important role in many applications such as medical data, image processing, fraud detection, intrusion detection, and so forth. An extensive variety of clustering based approaches have been developed to detect outliers. However they are by nature time consuming which restrict their utilization with real-time applications. Furthermore, outlier detection requests are handled one at a time, which means that each request is initiated individually with a particular set of parameters. In this paper, the first clustering based outlier detection framework, (On the Fly Clustering Based Outlier Detection (OFCOD)) is presented. OFCOD enables analysts to effectively find out outliers on time with request even within huge datasets. The proposed framework has been tested and evaluated using two real world datasets with different features and applications; one with 699 records, and another with five millions records. The experimental results show that the performance of the proposed framework outperforms other existing approaches while considering several evaluation metrics.

scholarly journals Comparison of Night, Day and 24 h Motor Activity Data for the Classification of Depressive Episodes

Diagnostics ◽  
2020 ◽  
Vol 10 (3) ◽  
pp. 162 ◽  
Author(s):  
Julieta G. Rodríguez-Ruiz ◽  
Carlos E. Galván-Tejada ◽  
Laura A. Zanella-Calzada ◽  
José M. Celaya-Padilla ◽  
Jorge I. Galván-Tejada ◽  
...  
Keyword(s):  
Data Mining ◽  
Motor Activity ◽  
Random Forest Classifier ◽  
Machine Learning Algorithms ◽  
World Population ◽  
Activity Data ◽  
The World ◽  
Depressive Episodes ◽  
Full Day

Major Depression Disease has been increasing in the last few years, affecting around 7 percent of the world population, but nowadays techniques to diagnose it are outdated and inefficient. Motor activity data in the last decade is presented as a better way to diagnose, treat and monitor patients suffering from this illness, this is achieved through the use of machine learning algorithms. Disturbances in the circadian rhythm of mental illness patients increase the effectiveness of the data mining process. In this paper, a comparison of motor activity data from the night, day and full day is carried out through a data mining process using the Random Forest classifier to identified depressive and non-depressive episodes. Data from Depressjon dataset is split into three different subsets and 24 features in time and frequency domain are extracted to select the best model to be used in the classification of depression episodes. The results showed that the best dataset and model to realize the classification of depressive episodes is the night motor activity data with 99.37% of sensitivity and 99.91% of specificity.

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