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All Journal IJIIS: International Journal of Informatics and Information Systems International Journal for Applied Information Management
Srisa-an, Chetneti
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Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Environmental Science Social Sciences

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A Classification of Internet Pornographic Images Srisa-an, Chetneti
International Journal of Informatics and Information Systems Vol 2, No 3: December 2019
Publisher : International Journal of Informatics and Information Systems

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijiis.v2i3.96

Abstract

According to Pornography Statistics,more than 34 percent of Internet users exposeto pornography. There are 12 percent of the total number of websites and 72 million monthly visitors.Internet pornography (Internet Porn) is addictive to teenagers and kids around the world. The normal practice is to block those websites or filter out pornographyfrom kids.In order to do so, researchers has to find a way to detect and classify first. The pixel features including YCbCr range, area of human skin are chosen as pornographyfeatures because of their easy acquisition. C4.5 (Data mining technique)is applied to construct a decision tree. The purpose of this paper is to classify pornography images in a simple if-then rule. The accuracy of experimental result is 85.2%.
Location-Based Mobile Community Using Ants-Based Cluster Algorithm Srisa-an, Chetneti
International Journal for Applied Information Management Vol. 1 No. 1 (2021): Regular Issue: April 2021
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijaim.v1i1.6

Abstract

A location based service (LBS) is widely used on modern smartphone around the world asits built-in features. Each smartphone can access a google API or map. People can therefore share their location (latitude and longitude) among friends. Many LBS spots can easily form “location based mobile community (LBMC).” Since the nodes are mobile, the community group changes dynamically and is unstructured. Ant-based clustering algorithm is a special kind of optimization technique which is highly suitable for finding the adaptive clustering for volatile networks. This Paper Aims To form a location based mobile community (LBMC) by using Ant-based clustering algorithm. Due to the mobile type community, a vanishing community problem is also stated in this paper. Instead of redo a whole algorithm again, we modify an original algorithm by applying a pheromone concept to handle a change. Our algorithm is named as ABCA & VP which stands for Ant-Based Clustering Algorithm with Vanishing problem. More than 5,000 samples from their latitude and longitude coordinates in Thailand. From an experiment, K-means clustering work well in small data size and low number of clusters. In Small size of data between 50 and 1000, our algorithm runs battery when a number of clusters reach 15 clusters. In a big data size (between 1,000 and 5,000 samples), our algorithm outperforms K-means clustering when a number of clusters reach 20 clusters.
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