• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.15 no.5
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• pp.193-199
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• 2015

Recently the artificial intelligence green house system, which collects automatically the informations of plants cultivation circumstances and controls the growing circumstances, is studied using temperature, humidity and illuminance sensors. In this paper, the inference for plants cultivation of optimum circumstance conditions is simulated on the internet bases by predicting the temperature, humidity and illuminance. On the IOT circumstances, the plant cultivation conditions of temperature, humidity and illuminance, using Arduino sensor, are transmitted to the manager on realtime and if the optimum condition of temperature and humidity for plant cultivation is not equal to the values, the system transmits automatically the SMS warning messages on realtime. Although the sudden climite conditions(snow, rain, hot weather) are occurred, the optimum condition of plant cultivation can be controlled. In this paper, using Fuzzy rules and WEKA TOOL, although the same flora temperature zone is used, the simulation is produced for the optimum value of temperature, humidity and illuminance for the zone.

• Convergence Security Journal
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• v.12 no.2
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• pp.61-70
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• 2012

The objective of this study is to achieve the most optimal data mining for effective analysis of false alarm event on mechanical security system. To perform this, this study searches the cause of false alarm and suggests the data conversion and analysis methods to apply to several algorithm of WEKA, which is a data mining program, based on statistical data for the number of case on movement by false alarm, false alarm rate and cause of false alarm. Analysis methods are used to estimate false alarm and set more effective reaction for false alarm by applying several algorithm. To use the suitable data for effective analysis of false alarm event on mechanical security analysis this study uses Decision Tree, Naive Bayes, BayesNet Apriori and J48Tree algorithm, and applies the algorithm by deducting the highest value.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.4
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• pp.169-174
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• 2019

In this study, I use surveillance data collection and data mining, clustered by clustering method, and use supervised learning to judge similarity. I aim to use feature extraction algorithms and supervised learning to analyze the suitability of the correlations of personality. After conducting the questionnaire survey, the researchers refine the collected data based on the questionnaire, classify the data sets through the clustering techniques of WEKA, an open source data mining tool, and judge similarity using supervised learning. I then use feature extraction algorithms and supervised learning to determine the suitability of the results for personality. As a result, it was found that the highest degree of similarity classification was obtained by EM classification and supervised learning by Naïve Bayes. The results of feature classification and supervised learning were found to be useful for judging fitness. I found that the accuracy of each Big-5 personality was changed according to the addition and deletion of the items, and analyzed the differences for each personality.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.1
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• pp.97-102
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• 2019

There are many difficulties to define because there is no systematic classification and analysis method using accurate criteria or numerical values for creative people. In order to solve this problem, this study attempts to analyze how to distinguish creative people and what kind of personality they have when distinguishing creative people. In this study, I first survey the Big 5 personality trait, classify and analyze the data set using the data mining tool WEKA, and then analyze the data set related to the creativity The goal is to analyze the features using various machine learning techniques. I use seven feature selection algorithms, select feature groups classified by feature selection algorithms, apply them to machine learning algorithms to find out the accuracy, and derive the results.

• Journal of Korean Society for Geospatial Information Science
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• v.18 no.3
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• pp.29-35
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• 2010

Soil organic carbon (SOC), being a help to forest formation and control of carbon dioxide in the air, is found to be an important factor by which global warming is influenced. Excavating the samples by whole area is very inefficient method to discovering the distribution of SOC. So, the development of suitable model for expecting the relative amount of the SOC makes better use of expecting the SOC. In the present study, a model based on a decision tree algorithm is introduced to estimate the amount of SOC along with accessing influencing factors such as altitude, aspect, slope and type of trees. The model was applied to a real site and validated by 10-fold cross validation using two softwares, See 5 and Weka. From the results given by See 5, it can be concluded that the amount of SOC in surface layers is highly related to the type of trees, while it is, in middle depth layers, dominated by both type of trees and altitude. The estimation accuracy was rated as 70.8% in surface layers and 64.7% in middle depth layers. A similar result was, in surface layers, given by Weka, but aspect was, in middle depth layers, found to be a meaningful factor along with types of trees and altitude. The estimation accuracy was rated as 68.87% and 60.65% in surface and middle depth layers. The introduced model is, from the tests, conceived to be useful to estimation of SOC amount and its application to SOC map production for wide areas.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2011.10a
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• pp.208-209
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• 2011

In this paper, we introduce a Naive Bayes learning algorithm for learning and reasoning in Map-Reduce model based environment. For this purpose, we use Apache Mahout to execute Distributed Naive Bayes on University of California, Irvine (UCI) benchmark data sets. From the experimental results, we see that Apache Mahout' s Distributed Naive Bayes algorithm is comparable to WEKA' s Naive Bayes algorithm in terms of performance. These results indicates that in the future Big Data environment, Map-Reduce model based systems such as Apache Mahout can be promising for machine learning usage.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.6
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• pp.13-20
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• 2022

Road conditions and weather conditions are very important factors in the case of traffic accident fatalities in fog and ice sections that occur on roads in winter. In this paper, a simulation was performed to estimate the traffic accident risk rate assuming traffic accident prediction data. In addition, in this paper, in order to reduce traffic accidents and prevent traffic accidents, factor analysis and traffic accident fatality rates were predicted using the WEKA data mining technique and TENSOR FLOW open source data on traffic accident fatalities provided by the Korea Transportation Corporation.

• Korean Journal of Artificial Intelligence
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• v.11 no.2
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• pp.19-27
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• 2023

In recent times, an exponential increase in Internet traffic has been observed as a result of advancing development of the Internet of Things, mobile networks with sensors, and communication functions within various devices. Further, the COVID-19 pandemic has inevitably led to an explosion of social network traffic. Within this context, considerable attention has been drawn to research on network traffic analysis based on machine learning. In this paper, we design and develop a new machine learning framework for network traffic analysis whereby normal and abnormal traffic is distinguished from one another. To achieve this, we combine together well-known machine learning algorithms and network traffic analysis techniques. Using one of the most widely used datasets KDD CUP'99 in the Weka and Apache Spark environments, we compare and investigate results obtained from time series type analysis of various aspects including malicious codes, feature extraction, data formalization, network traffic measurement tool implementation. Experimental analysis showed that while both the logistic regression and the support vector machine algorithm were excellent for performance evaluation, among these, the logistic regression algorithm performs better. The quantitative analysis results of our proposed machine learning framework show that this approach is reliable and practical, and the performance of the proposed system and another paper is compared and analyzed. In addition, we determined that the framework developed in the Apache Spark environment exhibits a much faster processing speed in the Spark environment than in Weka as there are more datasets used to create and classify machine learning models.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39C no.9
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• pp.811-819
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• 2014

Activity recognition is a key component in identifying the context of a user for providing services based on the application such as medical, entertainment and tactical scenarios. Instead of applying numerous sensor devices, as observed in many previous investigations, we are proposing the use of smartphone with its built-in multimodal sensors as an unobtrusive sensor device for recognition of six physical daily activities. As an improvement to previous works, accelerometer, gyroscope and magnetometer data are fused to recognize activities more reliably. The evaluation indicates that the IBK classifier using window size of 2s with 50% overlapping yields the highest accuracy (i.e., up to 99.33%). To achieve this peak accuracy, simple time-domain and frequency-domain features were extracted from raw sensor data of the smartphone.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.5B
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• pp.542-551
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• 2009

We proposed the context based user profile which is aware of its user's situation and based on user's situation it recommends personalized services. The user profile which consists of (context, service) pair can be acquired by the context and the service usage of a user; it then can be used to recommend personalized services for the user. In this paper, we show how they can be evolved without previously known user information so that not to violate privacy during the learning phase; in the result our user profile can be applied to any new environment without any modification to model only except context profiles. Using context-awareness based user profile, the service usage pattern of a user can be learned by the union of contexts and the preferred services can be recommended by the current environments. Finally, we evaluate the precision of proposed approach using simulation with data sets of UCI depository and Weka tool-kit.