scholarly journals A Distributed K-Means Segmentation Algorithm Applied to Lobesia botrana Recognition

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-14 ◽  
Author(s):  
José García ◽  
Christopher Pope ◽  
Francisco Altimiras
Keyword(s):  
Data Mining ◽  
Early Detection ◽  
Mobile Application ◽  
Image Data ◽  
Lobesia Botrana ◽  
Segmentation Algorithm ◽  
Gray Scale ◽  
Image Capture ◽  
Major Pest ◽  
Novel Method

Early detection of Lobesia botrana is a primary issue for a proper control of this insect considered as the major pest in grapevine. In this article, we propose a novel method for L. botrana recognition using image data mining based on clustering segmentation with descriptors which consider gray scale values and gradient in each segment. This system allows a 95 percent of L. botrana recognition in non-fully controlled lighting, zoom, and orientation environments. Our image capture application is currently implemented in a mobile application and subsequent segmentation processing is done in the cloud.

You are where you go, the commodification of daily life through ‘location’

2017 ◽  
Vol 49 (12) ◽  
pp. 2702-2717 ◽  
Author(s):  
Jim Thatcher
Keyword(s):  
Data Mining ◽  
Everyday Life ◽  
Mobile Application ◽  
Daily Life ◽  
Digital Data ◽  
Late Capitalism ◽  
User Intent ◽  
Data Object ◽  
Other Information ◽  
Sensor Information

Recent years have seen an explosion in the investment into and valuation of mobile spatial applications. With multiple applications currently valued at well over one billion U.S. dollars, mobile spatial applications and the data they generate have come to play an increasingly significant role in the function of late capitalism. Empirically based upon a series of interviews conducted with mobile application designers and developers, this article details the creation of a digital commodity termed ‘location.’ ‘Location’ is developed through three discursive poles: Its storing of space and time as digital data object manipulable by code, its spatial and temporal immediacy, and its ability to ‘add value’ or ‘tell a story’ to both end-users and marketers. As a commodity it represents the sum total of targeted marking information, including credit profiles, purchase history, and a host of other information available through data mining or sensor information, combined with temporal immediacy, physical location, and user intent. ‘Location’ is demonstrated to exist as a commodity from its very inception and, as such, to be a key means through which everyday life is further entangled with processes of capitalist exploitation.

scholarly journals Cloud based mobile solution for early detection of Skin Cancer using Artificial Intelligence

2021 ◽  
pp. 312-324
Author(s):  
Pawan Sonawane ◽  
Sahel Shardhul ◽  
Raju Mendhe
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Skin Cancer ◽  
Early Detection ◽  
Transfer Learning ◽  
Network Model ◽  
Neural Network Model ◽  
Mobile Application ◽  
Low Cost ◽  
Skin Tumors

The vast majority of skin cancer deaths are from melanoma, with about 1.04 million cases annually. Early detection of the same can be immensely helpful in order to try to cure it. But most of the diagnosis procedures are either extremely expensive or not available to a vast majority, as these centers are concentrated in urban regions only. Thus, there is a need for an application that can perform a quick, efficient, and low-cost diagnosis. Our solution proposes to build a server less mobile application on the AWS cloud that takes the images of potential skin tumors and classifies it as either Malignant or Benign. The classification would be carried out using a trained Convolution Neural Network model and Transfer learning (Inception v3). Several experiments will be performed based on Morphology and Color of the tumor to identify ideal parameters.

Distance Based Pattern Driven Mining for Outlier Detection in High Dimensional Big Dataset

2022 ◽  
Vol 13 (1) ◽  
pp. 1-17
Author(s):  
Ankit Kumar ◽  
Abhishek Kumar ◽  
Ali Kashif Bashir ◽  
Mamoon Rashid ◽  
V. D. Ambeth Kumar ◽  
...  
Keyword(s):  
Data Mining ◽  
Comparative Analysis ◽  
Outlier Detection ◽  
Credit Card ◽  
High Dimensional ◽  
Work Efficiency ◽  
Average Value ◽  
Novel Method ◽  
Detection Of Outliers ◽  
Better Than

Detection of outliers or anomalies is one of the vital issues in pattern-driven data mining. Outlier detection detects the inconsistent behavior of individual objects. It is an important sector in the data mining field with several different applications such as detecting credit card fraud, hacking discovery and discovering criminal activities. It is necessary to develop tools used to uncover the critical information established in the extensive data. This paper investigated a novel method for detecting cluster outliers in a multidimensional dataset, capable of identifying the clusters and outliers for datasets containing noise. The proposed method can detect the groups and outliers left by the clustering process, like instant irregular sets of clusters (C) and outliers (O), to boost the results. The results obtained after applying the algorithm to the dataset improved in terms of several parameters. For the comparative analysis, the accurate average value and the recall value parameters are computed. The accurate average value is 74.05% of the existing COID algorithm, and our proposed algorithm has 77.21%. The average recall value is 81.19% and 89.51% of the existing and proposed algorithm, which shows that the proposed work efficiency is better than the existing COID algorithm.

Improved Privacy

2011 ◽  
pp. 295-316
Author(s):  
K. Abumani ◽  
R. Nedunchezhian
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Empirical Analysis ◽  
Time Complexity ◽  
Strategic Decision ◽  
Strategic Decision Making ◽  
Sensitive Information ◽  
Data Mining Techniques ◽  
Novel Method ◽  
Mining Tools

Data mining techniques have been widely used for extracting non-trivial information from massive amounts of data. They help in strategic decision-making as well as many more applications. However, data mining also has a few demerits apart from its usefulness. Sensitive information contained in the database may be brought out by the data mining tools. Different approaches are being utilized to hide the sensitive information. The proposed work in this article applies a novel method to access the generating transactions with minimum effort from the transactional database. It helps in reducing the time complexity of any hiding algorithm. The theoretical and empirical analysis of the algorithm shows that hiding of data using this proposed work performs association rule hiding quicker than other algorithms.

Algorithms for Data Mining

2008 ◽  
pp. 1301-1319
Author(s):  
Tadao Takaoka ◽  
Nigel K.L. Pope ◽  
Kevin E. Voges
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Image Data ◽  
Regression Trees ◽  
Clustering Techniques ◽  
Data Mining Algorithms ◽  
Mining Algorithms

In this chapter, we present an overview of some common data mining algorithms. Two techniques are considered in detail. The first is association rules, a fundamental approach that is one of the oldest and most widely used techniques in data mining. It is used, for example, in supermarket basket analysis to identify relationships between purchased items. The second is the maximum sub-array problem, which is an emerging area that is yet to produce a textbook description. This area is becoming important as a new tool for data mining, particularly in the analysis of image data. For both of these techniques, algorithms are presented in pseudo-code to demonstrate the logic of the approaches. We also briefly consider decision and regression trees and clustering techniques.

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